ARTIFICIAL INTELLIGENCE IN THEORY AND PRACTICE · 2013-07-23 · ARTIFICIAL INTELLIGENCE IN THEORY AND PRACTICE IFIP 19th World Computer Congress, TC 12: IFIPAI2006 Stream, August

ARTIFICIAL INTELLIGENCE IN THEORY AND PRACTICE

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ARTIFICIAL INTELLIGENCE IN THEORY AND PRACTICE

IFIP 19th World Computer Congress, TC 12: IFIPAI2006 Stream, August 21-24, 2006, Santiago, Chile

Edited by

Max Bramer University of Portsmouth, UK

Springer

Library of Congress Control Number: 2006927831

Artificial Intelligence in Theory and Practice

Edited by M. Bramer

p. cm. (IFIP International Federation for Information Processing, a Springer Series in Computer Science)

ISSN: 1571-5736/ 1861-2288 (Internet) ISBN: 10: 0-387-34654-6 ISBN: 13: 9780-387-34654-0 elSBN: 10: 0-387-34747-X

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Contents

Foreword xi

Acknowledgements xiii

Paper Sessions

Knowledge and Information Management

Can Common Sense uncover cultural differences in computer 1 applications?

Junia Anacleto, Henry Lieherman, Marie Tsutsumi, Vania Neris, Aparecido Carvalho et al.

Artificial Intelligence and Knowledge Management 11 Hugo Cesar Hoeschl and Vania Bar cellos

Agents 1

The lONWI algorithm: Learning when and when not to 21 interrupt

Silvia Schiaffino andAnalia Amandi

Formal Analysis of the Communication of Probabilistic 31 Knowledge

Joao Carlos Gluz, Rosa Maria Vicari, Cecilia Flores and Louise Seixas

Detecting and Repairing Anomalous Evolutions in Noisy 41 Environments: Logic Programming Formalization and Complexity Results

Fabrizio Angiulli, Gianluigi Greco and Luigi Palopoli

Adding Semantic Web Services Matching and Discovery 51 Support to the MoviLog Platform

Cristian Mateos, Marco Crasso, Alejandro Zunino and Marcelo Campo

Learning Browsing Patterns for Context-Aware 61 Recommendation

Daniela Godoy and Analia Amandi

Applying Collaborative Filtering to Reputation Domain: a 71 model for more precise reputation estimates in case of changing behavior by rated participants

Alexandre Lopes, Ana Cristina Bicharra Garcia

Integration of AI with other Technologies

Combine Vector Quantization and Support Vector Machine 81 for Imbalanced Datasets

Ting Yu, John Debenham, Tony Jan and Simeon Simoff

Ontology Support for Translating Negotiation Primitives 89 Maricela Bravo, Maximo Lopez, Azucena Monies, Rene Santaolaya, Raul Pinto, Joaquin Perez

Statistical Method of Context Evaluation for Biological 99 Sequence Similarity

Alina Bogan-Marta, loannis Pitas and Kleoniki Lyroudia

Biological inspired algorithm for Storage Area Networks 109 (ACOSAN)

Anabel Fraga Vazquez

Neural Nets

Radial Basis Functions Versus Geostatistics in Spatial 119 Interpolations

Cristian Rusu, Virginia Rusu

Neural Networks applied to wireless communications 129 Georgina Stegmayer, Omar Chiotti

Anomaly Detection using prior knowledge: application to 139 TCP/IP traffic

Alberto Carrascal, Jorge Couchet, Enrique Ferreira and Daniel Manrique

Artificial Intelligence in Theory and Practice vii

A study on the ability of Support Vector Regression and 149 Neural Networks to Forecast Basic Time Series Patterns

Sven Crone, Jose Guajardo and Richard Weber

Neural Plasma 159 Daniel Berrar and Werner Dubitzky

Knowledge Acquisition and Data Mining

Comparison of SVM and some Older Classification 169 Algorithms in Text Classification Tasks

Fabrice Colas, Pavel Brazdil

An automatic graph layout procedure to visualize correlated 179 data

Mario Inostroza-Ponta, Regina Berretta, Alexandre Mendes, Pablo Moscato

Knowledge Perspectives in Data Grids 189 Luis Eliecer Cadenas and Emilio Hernandez

On the Class Distribution Labelling Step Sensitivity of Co- 199 training

Edson Takashi Matsubara, Maria Carolina Monard and Ronaldo Cristiano Prati

Two new feature selection algorithms with Rough Sets 209 Theory

Yaile Caballero, Rafael Bello, Delia Alvarez, Maria M. Garcia

Evolutionary Computation

Global Convexity in the Bi-Criteria Traveling Salesman 217 Problem

Marcos Villagra, Benjamin Bardn and Osvaldo Gomez

Evolutionary Algorithm for State Encoding 227 Valery Sklyarov and louliia Skliarova

Hypercube Frame Work for ACO applied to timetabling 237 Franklin Johnson, Broderick Crawford and Wenceslao Palma

Multitree-Multiobjective Multicast Routing for Traffic 247 Engineering

Joel Prieto, Benjamin Bardn, Jorge Crichigno

Speech and Natural Language

Road Segment Identification in Natural Language Text 257 Ahmed Y. Tawfik and Lawrence Barsanti

Learning Discourse-new References in Portuguese Texts 267 Sandra Collovini and Renata Vieira

Analysing Definition Questions by Two Machine Leaming 277 Approaches

Carmen Martinez and A. Lopez Lopez

Fuzzy Rule-Based Hand Gesture Recognition 285 Benjamin Callejas Bedregal, Antonio Carlos da Rocha Costa and Gragaliz Pereira Dimuro

Comparison of distance measures for historical spelling 295 variants

Sebastian Kempken, Wolfram Luther and Thomas Pilz

Industrial Applications of AI

Patterns in Temporal Series of Meteorological Variables 305 Using SOM & TDIDT

Marisa Cogliati, Paola Britos and Ramon Garcia-Martinez

Applying Genetic Algorithms to Convoy Scheduling 315 Edward M. Robinson, Ernst L. Leiss

A GRASP algorithm to solve the problem of dependent tasks 325 scheduling in different machines

Manuel Tupia Anticona

Artificial Intelligence in Theory and Practice ix

A support vector machine as an estimator of mountain 335 papaya ripeness using resonant frequency or frequency centroid

Per Bj. Bro, Christophe Rosenberger and Helene Laurent, Carlos Gaete-Eastman, Mario Fernandez and Alejandra Moya-Leon

FieldPlan: Tactical Field Force Planning in BT 345 Mathias Kern, George Anim-Ansah, Gilbert Owusu and Chris Voudouris

An Agent Solution to Flexible Planning and Scheduling of 355 Passenger Trips

Claudio Cubillos and Franco Guidi-Polanco

Machine Vision

Facial expression recognition using shape and texture 365 information

Irene Kotsia and loannis Pitas

Limited Receptive Area neural classifier for texture 375 recognition of metal surfaces

Oleksandr Makeyev, Tatiana Baidyk andAnabel Martin

A Tracking Framework for Accurate Face Localization 385 Ines Cherif, Vassilios Solachidis and loannis Pitas

A Combination of Spatiotemporal ICA and Euclidean 395 Features for Face Recognition

Jiajin Lei, Tim Lay, Chris Wetland, Chao Lu

Agents 2

Three Technologies for Automated Trading 405 John Debenham and Simeon Simoff

Modeling Travel Assistant Agents: a graded BDI approach 415 Ana Casali, Lluis Godo and Carles Sierra

e-Tools: An agent coordination layer to support the mobility 425 of persons with disabilities.

Cristian Barrue, Ulises Cortes, Antonio B. Martinez, Josep Escoda, Roberta Annicchiarico and Carlo Caltagirone

Expert Systems

Conceptualization Maturity Metrics for Expert Systems 435 Ovind Hauge, Paola Britos and Ramon Garcia-Martinez

Toward developing a tele-diagnosis system on fish disease 445 Daoliang Li, Wei Zhu, Yanqing Duan, Zetian Fu

A new method for fish-disease diagnostic problem solving 455 based on parsimonious covering theory and fuzzy inference model

Jiwen Wen, Daoliang Li, Wei Zhu and Zetian Fu

Effective Prover for Minimal Inconsistency Logic 465 Adolfo Gustavo Serra Seca Neto and Marcelo Finger

Identification of Important News for Exchange Rate 475 Modeling

Debbie Zhang, Simeon J. Simoffand John Debenham

Planning and Scheduling

Autonomous Search and Rescue Rotorcraft Mission 483 Stochastic Planning with Generic DBNs

Florent Teichteil-Konigsbuch and Patrick Fabiani

Solving multi-objective scheduling problems-An integrated 493 systems approach

Martin Josef Geiger

Foreword

The papers in this volume comprise the refereed proceedings of the conference 'Artificial Intelligence in Theory and Practice' (IFIP AI 2006), which formed part of the 19th World Computer Congress of IFIP, the International Federation for Information Processing (WCC-2006), in Santiago, Chile in August 2006.

The conference is organised by the IFIP Technical Committee on Artificial Intelligence (Technical Committee 12) and its Working Group 12.5 (Artificial Intelligence Applications).

All papers were reviewed by at least two members of our Programme Committee. The best papers were selected for the conference and are included in this volume. The international nature of IFIP is amply reflected in the large number of countries represented here.

The conference featured invited talks by Rose Dieng, John Atkinson, John Debenham and myself. IFIP AI 2006 also included the Second IFIP Symposium on Professional Practice in Artificial Intelligence, organised by Professor John Debenham, which ran alongside the refereed papers. I should like to thank the conference chair. Professor Debenham for all his efforts in organising the Symposium and the members of our programme committee for reviewing an unexpectedly large number of papers to a very tight deadline.

This is the latest in a series of conferences organised by IFIP Technical Committee 12 dedicated to the techniques of Artificial Intelligence and their real-world applications. The wide range and importance of these applications is clearly indicated by the papers in this volume. Further information about TCI 2 can be found on our website http://www.ifiptcl2.org.

Max Bramer Chair, IFIP Technical Committee on Artificial Intelligence

Acknowledgements

Conference Organising Committee

Conference General Chair

John Debenham (University of Technology, Sydney, Australia)

Conference Program Chair

Max Bramer (University of Portsmouth, United Kingdom)

Executive Programme Committee

Max Bramer (University of Portsmouth, United Kingdom) John Debenham (University of Technology, Sydney, Australia) Vladan Devedzic (University of Belgrade, Serbia and Montenegro) Eunika Mercier-Laurent (KIM, France)

Programme Committee

Agnar Aamodt (Norwegian University of Science and Technology, Norway)

Analia Amandi (ISISTAN Research Institute, Argentina) Lora Aroyo (Eindhoven University of Technology, The Netherlands) Stefania Bandini (University of Milan, Italy) Max Bramer (University of Portsmouth, United Kingdom) Krysia Broda (Imperial College London, United Kingdom) Zdzislaw Bubnicki (Wroclaw University of Technology, Poland) Luigia Carlucci Aiello (Universita di Roma La Sapienza, Italy) Monica Crubezy (Stanford University, USA) John Debenham (University of Technology, Sydney, Australia) Joris Deguet (CNRS - IMAG Institute, France) Evangelos Dellis (Inst, of Informatics & Telecommunications, NCSR,

Athens, Greece) Yves Demazeau (CNRS - IMAG Institute, France) Vladan Devedzic (University of Belgrade, Serbia and Montenegro)

Tharam Dillon (University of Technology, Sydney, Australia) John Domingue (The Open University, United Kingdom) Anne Dourgnon-Hanoune (EDF, France) Gintautas Dzemyda (Institute of Mathematics and Informatics,

Lithuania) Henrik Eriksson (Linkoping University, Sweden) Matjaz Gams (Slovenia) Ana Garcia-Serrano (Technical University of Madrid, Spain) Daniela Godoy (ISISTAN Research Institute, Argentina) Fedja Hadzic (University of Technology, Sydney, Australia) Andreas Harrer (University Duisburg-Essen, Germany) Timo Honkela (Helsinki University of Technology, Finland) Werner Horn (Medical University of Vienna, Austria) Tony Jan (University of Technology, Sydney, Australia) Kostas Karpouzis (National Technical University of Athens, Greece) Dusko Katie (Serbia and Montenegro) Ray Kemp (Massey University, New Zealand) Dr. Kinshuk (Massey University, New Zealand) Joost N. Kok (Leiden University, The Netherlands) Stasinos Konstantopoulos (Inst, of Informatics &

Telecommunications, NCSR, Athens, Greece) Jasna Kuljis (Brunei University, United Kingdom) Daoliang Li (China Agricultural University, Beijing) Ilias Maglogiannis (University of Aegean, Samos, Greece) Suresh Manandhar (University of York, United Kingdom) Ramon Lopez de Mantaras (Spanish Council for Scientific Research) Brian Mayoh (University of Aarhus, Denmark) Dimitri Melaye (CNRS - IMAG Institute, France) Eunika Mercier-Laurent (KIM, France) Tanja Mitrovic (University of Canterbury, Christchurch, New

Zealand) Riichiro Mizoguchi (Osaka University, Japan) Zsolt Nagy (Budapest University of Technology and Economics,

Hungary) Pavol Navrat (Slovak University of Technology in Bratislava,

Slovakia) Erich Neuhold (RSA-DME) Bemd Neumann (University of Hamburg, Germany) Daniel O'Leary (University of Southern California, USA) Andrea Omicini (Alma Mater Studiorum-Universita di Bologna, Italy) Mihaela Oprea (University of Ploiesti, Romania) Stavros Perantonis (Inst, of Informatics & Telecommunications,

NCSR, Athens, Greece) Guillaume Piolle (CNRS - IMAG Institute, France) Alun Preece (University of Aberdeen, United Kingdom) Abdel-Badeeh M. Salem (Ain Shams University, Egypt) Demetrios Sampson (University of Piraeus & CERTH, Greece)

Artificial Intelligence in Theory and Practice xv

M Sasikumar (C-DAC, Mumbai, India) Silvia Schiaffino (ISISTAN Research Institute, Argentina) Mauricio Solar (Chile) Constantine Spyropoulos (Inst, of Informatics & Telecommunications,

NCSR, Athens, Greece) Steffen Staab (University of Koblenz, Germany) Olga Stepankova (Czech Technical University in Prague, Czech

Republic) Peter Szeredi (Budapest University of Technology and Economics,

Hungary) Vagan Terziyan (University of Jyvaskyla, Finland) Nicolas Kemper Valverde (National Autonomous University of

Mexico) Wiebe van der Hoek (University of Liverpool, United Kingdom) Marie-Helene Verrons (CNRS - IMAG Institute, France) Virginia Daniela Yannibelli (ISISTAN Research Institute, Argentina) Zdenek Zdrahal (The Open University, United Kingdom) Jianhan Zhu (The Open University, United Kingdom)

Can Common Sense uncover cultural differences in computer applications?

Junia Anacleto', Henry Lieberman ,̂ Marie Tsutsumi',Vania Neds', Aparecido Carvalho', Jose Espinosa ,̂ Muriel Godoi' and Silvia Zem-

Mascarenhas' 1 Advanced Interaction Laboratory - LIA

UFSCar - Rod. Washigton Luis KM 235 - Sao Carios - SP - Brazil (junia, marietsutsumi, vania, fabiano, muriel_godoi}@dc.ufscar.br;

[email protected] WWW home page: http://lia.dc.ufscar.br

2 MIT Media Laboratory 20 Ames St., 384A Cambridge MA 02139

{lieber, jhe} @media.mit.edu WWW home page: http://www.media.mit.edu

Abstract. Cultural differences play a very important role in matching computer interfaces to the expectations of users from different national and cultural backgrounds. But to date, there has been httle systematic research as to the extent of such differences, and how to produce software that automatically takes into account these differences. We are studying these issues using a unique resource: Common Sense knowledge bases in different languages. Our research points out that this kind of knowledge can help computer systems to consider cultural differences. We describe our experiences with knowledge bases containing thousands of sentences describing people and everyday activities, collected from volunteer Web contributors in three different cultures: Brazil, Mexico and the USA, and software which automatically searches for cultural differences amongst the three cultures, alerting the user to potential differences.

1. Introduction

The answer to the title question, according to our preliminary results, is yes. However, to uncover cultural differences is not a simple task. Many researchers have pointed that cultural differences should be considered in the design of interactive systems [13, 7]. Culture is a shared meaning system which forms a framework for problem solving and behavior in everyday life. Individuals communicate with each other by assigning meaning to messages based on their prior beliefs, attitudes, and values [7].

Please use the following formatwhen citing this chapter:

Anacleto, J., Liebennan, H., Tsutsumi, M., Neris, Y, Can'alho, A. et. al., 2006, in IFIP Intemational Federation for Informa

tion Processing, Volume 217, Artificial Intelligence in Theory and Practice, ed. M. Bramer, (Boston: Springer), pp. 1-10.

2 Artificial Intelligence in Theory and Practice

The cultural differences express the "world vision" a group of people have. This vision is expressed in the simple activities that people do everyday. Arguably the most general and widely applicable kind is knowledge about the everyday world that is possessed by most people in a given culture — what is widely called 'common sense knowledge'. While 'common sense' to the ordinary people is related to 'good judgment' as a synonymous, the Artificial Intelligence community uses the term "common sense" to refer to the millions of basic facts and understandings that most people have. For example, the lemon is sour; to open a door, you must usually first turn the doorknob; if you forget someone's birthday, they may be unhappy with you.

Common sense knowledge, thus defined, spans a huge portion of human experience, encompassing knowledge about the spatial, physical, social, temporal and psychological aspects of typical everyday life. Common sense is acquired from the interaction with the environment. Changing the environment changes the perception of common sense and is one of the reasons why different and diverse cultures exist. This conception of common sense is building ontology about everyday life based on the shared experiences of a community [12].

The challenge is to try to represent cultural knowledge in the machine, and have interfaces that automatically and dynamically adapt to different cultures. While fully implementing this goal is still out of reach, this paper takes some first steps.

We have collected large knowledge bases representing Commonsense knowledge in three cultures: Brazil, Mexico and the USA. Comparison between these knowledge bases gives us a basis for automatically discovering differences between cultures, and finding analogies from one culture to another. Software for cultural comparison is useful in many contexts. For example, by those who want to develop systems focusing on a specific user group (e.g. a teacher who consults the common sense database to prepare a specific instructional content); by those who want to develop systems which use the cultural knowledge stored in the knowledge bases (e.g search engines that consider the context); and by those who want to facilitate communication between people, providing mutual knowledge about their cultures.

In this context, the main purpose of this work, partially supported by TIDIA-Ae FAPESP project, proc no. 03/08276-3, and by CAPES, is to evaluate how the cultural differences can be recognized in the databases that store common sense. For that, we select a theme that irequently appeared in the Brazilian knowledge base -food. Considering that eating habits express culture (Fl) and common sense affects eating habits (F2), we could say that common sense expresses culture. Fl is taken as true and so we should demonstrate F2.

It is important that the reader understands that we make no claim to have fully described a national culture, or to have fully captured cultural differences. Instead, the goal is simply to get some useful representation of culture and common knowledge, where otherwise the computer would have none. We believe that our contribution is the first attempt to systematically study the extent and nature of cultural differences; to represent cultural differences in machine-readable form; and to present an example of software that searches for such differences and provides inter-cultural translations automatically.

The next section presents how data are collected in the Open Mind Common Sense (OMCS) knowledge bases. To give the reader some idea of how knowledge in the various databases compares, we present some example comparisons for the food

Artificial Intelligence in Theory and Practice ;

domain. We then discuss the prototype agent for finding cultural differences, followed by conclusion and future work.

2. The Open Mind Common Sense Approach for Gathering and Using Common Sense Facts

Arguably the most general and widely applicable kind of knowledge about the everyday world is the knowledge we typically assume is possessed by ordinary people in a given culture — what is widely called 'common sense knowledge'. One way that AI has used to represent this knowledge is by simple sentences asserting such facts (pioneered by Doug Lenat's CYC project). For example, a lemon is sour; to open a door, you must usually first turn the doorknob; if you forget someone's birthday, they may be unhappy with you. Common sense knowledge, thus defined, spans a huge portion of human experience, encompassing knowledge about the spatial, physical, social, temporal and psychological aspects of typical everyday life.

Since every ordinary person has the common sense that computers lack, why not involve everyone in building the knowledge base that is necessary to give computers what they need to be able of common sense reasoning? Nowadays, it is easy to reach lots of people through the Internet. For gathering the common sense data some Open Mind Common Sense websites were built. As the name suggests, the Open Mind Common Sense (OMCS) sites are open. Everyone who wishes to help can contribute with his or her knowledge.

The data are stored in the OMCS database as simple statements in natural language. However, for machine use, it is necessary to put them in a representation that allows machines to make practical inferences and analogies. For that, the data are submitted to a natural-language parser that generates a set of normalized nodes that are semantically related, composing a semantic network. A better understanding about how this semantic network is generated is presented by Liu [12].

Once the semantic network is ready, applications can be developed using the common sense knowledge provided by different users.

\ ' / I .t •,,1', ..I . Ttr?.!™.-™. know

• • . . • • ^ . ' j ; . : I-.•:'•••• J . " ."•?. -hys:"u"Ti7^zr.ii'r:,,',i

Figure 1. Common Sense sentences from Portuguese- and English-speaking contributors


3. Common Sense and Eating Habits In this section, we give the reader a glimpse of what can be compared between

the sites. Again, the idea is not to present a definitive scientific survey about such issues as what people actually eat for breakfast in Brazil versus the USA. The knowledge, after all, comes from members of the general public who may have legitimate disagreements or differing experiences about these issues. The idea is that Common Sense collects plausible (rather than completely accurate) answers about these questions, which can lead to plausible assumptions about cultural differences.

Considering the redundancies in our data, we selected categories that appeared in higher frequency in each base. Some of these data were presented in [1]. The categories are presented bellow.

Time for meals One of the most common themes in the knowledge bases is time for meals. Table

1 shows what is considered common sense for most of the collaborators.

Table l.Time for meals.

Lunch Dinner

Brazil

11:30 to 13:00 18:30 to 20:00

Mexico

14:00 to 16:00 20:00 to 21:00

USA

12:00 to 14:00 18:00 to 19:00

Here it is interesting to note that meals in Mexico are the latest ones. Although in Brazil and the USA, meals happen at a similar hour, in Mexico it seems to be common to have lunch after 14:00.

What do people eat in each meal? Differences between what is eaten in each meal also can be noticed. Table 2

shows what seems to be considered common sense about what to eat in each meal. It is possible to notice that Brazilian people prepare lighter food at breakfast.

Also Mexican people seem to like food make with flour. Concerning desserts, Brazilian people associate ice cream as something cooling, and are reluctant to eat it in Brazil's (relatively mild) winter. On the other hand, American people seem to prefer pies and other baked desserts.

Table 2. What do they eat in each meal?

Breakfast

Lunch

Dinner

Brazil

bread

rice. beans. meat.

salad, egg

rice and beans.

Mexico

tamales, eggs with hot sauce chicken with mole, roast

meat, pastries. chilaquiles,

barbacoa, tacos tamales and

atole.

USA

pancakes. bagels

hamburger, hot dog, pizza. sandwiches

steak and eggs. baked chicken,


Dessert

soup, salad,

sandwich

ice cream, fruit, candy

Food for special occasions

quesadillas. coffee and

cookies, bread with bean

rice with milk, churros with

chocolate, nuts with honey (crowbar).

sweet coconut

clam chowder. mashed potatoes

pumpkin pie. apple pie, ice cream, cheese

cake

Christmas and parties were topics that collaborators remembered too. Table 3 shows the main types of food cited for this occasions. It is interesting to notice that in Brazil and Mexico it seems to be common have salty food for Christmas while in the USA sweet dishes seem to be more appreciated. On the other hand, beer seems to be appreciated at parties in all three countries.

Table 3. Food for special occasions.

Party

Christmas

Brazil

snack, candy, cake, meat,

beer turkey, pork.

lamb

Mexico beer, tequila

romeritos, codfish. spaghetti

USA

beer, vodka

cranberry sauce.

pineapple salad, frozen

Christmas Pudding

4 Using Cultural Knowledge in Interactive Applications We believe the cultural differences stored in the common sense bases can be

helpful in (a) helping those who want to consider these differences in the development of interactive systems; (b) facilitating the interaction of different users by applications that use this common sense; and (c) facilitating the communication between people. Here we point out how developers involved in the situations cited above can use the cultural differences stored in common sense knowledge bases.

Developing systems considering cultural differences: Human Computer Interaction research raises further questions about how to understand culture and how it can and should affect user-interface design. Attributes as attraction, dynamism, activity, level of expertise, faith, intentions, locality, social validation, preferences, and scarcity have different weightings in different cultures [2]. Consequently, user-interface developers face further challenges [13]. Many questions still persist while talking about considering cultural differences in the design of interactive systems. Marcus [13] raises some questions: Are our notions of


usability culturally biased? How should culture differences relate to persuasion and establishment of trust in Web sites and Web-based applications? How should culture dimensions relate to established dimensions of intelligence and change your thinking about online help, documentation, and training? How do culture differences relate to new insight about cognition differences? Do these differences change your thinking about user search strategies, mental models, and navigation? The only consensus seems to be that these attributes have different values and are key characteristics of the cultures to which they belong [4]. Despite the importance of these questions, some developers still face an uphill battle to get budgets for culture-oriented research and development accepted, to find and allocate the human resources, and to achieve project success [13]. In this context, collecting these "world views" and making them available for everyone that wants to develop a user-interface, can be expensive and laborious. The use of the Internet and the collaboration of millions of people allow knowledge bases to reflect actual cultural knowledge without cost, as anyone can have access to the database at the sites.

Developing systems which consider cultural differences: As the complexity of computer applications grows, it may be that the only way to make applications more helpful and avoid stupid mistakes and annoying interruptions is to make use of common sense knowledge. Cellular telephones should know enough to witch to vibrate mode if you're at the symphony. Calendars should warn you if you try to schedule a meeting at 2 AM or plan to take a vegetarian to a steak house. Cameras should realize that if you took a group of pictures within a span of two hours, at round the same location, they are probably of the same event [8]. In the web context, the necessity of using common sense knowledge becomes even more evident. The number of web pages available on Internet increases day after day, and consequently, finding relevant information becomes more and more a difficult task [3]. Also, Web Search tools do not do a very good job of discerning individuals' search goals [16]. However, when we consider communities of people with common interests, it is possible to improve the quality of the query results using knowledge extracted from common sense databases and observing behaviors of people of same culture. When a user submits a query, the cultural aspects suggest specific information exploiting previous observations about the behavior of other users when they asked similar queries. Different users may merit different answers to the same query [3]. As cultural differences can be detected in common sense bases, search engines that attempt to leverage common sense have a great opportunity to reflect cultural differences in their results.

Developing systems which facilitate communication between people by showing cultural differences: Communication between people from different cultures is a field which presents many interesting aspects. To show that common sense can help showing the cultural differences, a prototype of a mail client was developed. The application has an agent that keeps watching what the user is typing, while makes commentaries on the differences in the grounding that can lead to possible misunderstandings. The system also uses these differences to calculate analogies for concepts that evoke the same social meaning in those cultures. We focus this prototype on the social interaction among people in the context of eating habits, but it could scale to other domains. The system's interface has three sections, as can be seen in Figure 2. The first one - at the upper left - is the information for

Artificial Intelligence in Theory and Practice 1

the email addresses and the subject, the second one - at the upper right - is where the agent posts its commentaries about the cultural differences and the third part - the lower part - is the body of the message. The second section has four subsections: the upper one shows the analogies that the agent found and the other three show the data that are not suitable for analogy. For example, in our screen shot, the third label for the Mexican culture - Mexicans thinks that dinner is coffee and cookies - and the second for American culture - Americans think that dinner is baked chicken - cannot make a meaningful analogy even if they differ only in one term.

Dinner Is for Brazilians what lunch is for Mexicans Pinner Is for Americans what dinner is for Brazilians

Brazilians think that dinner is nc® and eeans Brazifians thinlctha) dinner is saiad, meat and potatoes

Brazilians make dinner between 6:30 and 8:00 PM BrgziSans'think dinner is soup

Mexicans, think that dinner is qyesacfiiias Mexicans mal-.e dmne"- beiwaen 8:00 and 9:00 PM Mexicans think that dinnsi is coffee and cookies - Mexicans tointc that diniier is bread 'wilh heans

Americans thinks that dinner is steak and eggs Americans think that dinner is bai«e chiclfsn

Americans iUn'f. that dinner is smash potatoes Americans make dinner between 6:00 and 7:00 PM

im going to have a dinner next Fnday in my place

Figure 2. A screen shot of the system.

In order to make the cultural analogies, the system uses four semantic networks. The OMCSNet [10] semantic network (OMCSNet.OM), which was mined from the Open Mind corpus, is used as the core engine because it provides tools for context expansion and is especially designed for working with Open Mind Common Sense databases. The other three databases are culturally specific; they have knowledge about the Brazilian, Mexican and North-American culture - these semantic networks are called OMCSNet.BR, OMCSNet.MX and OMCSNet.US respectively. The OMCSNet.BR was built from data mined from the Brazilian Open Mind Common Sense database. In the American and Mexican cases, the statements were already in English language. In the Brazilian case, the statements were originally in Portuguese. For this project, a small group of statements related to eating habits were selected and freely translated to English to be parsed.

The calculation of cultural analogies is divided into eight steps. Data retrieval (step 1): The first thing that the system does is to use the NLP

package MontyLingua [9] to get the relevant concepts of the mail. This information is presented as tuples of (verb subject direct_object indirect_object). In the example above, the NLP tool produces the output ("have" "I" "dinner" "my place").

Context retrieval (step 2): Then, we use each direct and indirect object of the tuples from the previous step to query the OMCSNet.OM for the relevant concepts. Querying this network first gives us some query expansion of "culturally

i Artificial Intelligence in Theory and Practice

independent" relations. That is, the base OMCSNet.OM, as our largest and most diverse collection, is taken as the "standard" to which the other databases are compared. Since OMCSNet.OM itself has some cultural bias, it would be better, once we have collected enough knowledge bases in other languages, to create a "worldwide" knowledge base by removing all culturally-specific statements, as a basis for comparison. The result of this query is used to get the context in the culturally specific nets. At the end of this stage the output was ranked using two criteria: the first one prefers the concepts resulting for the cultural databases, and the second is to rank first the concepts that come from the part of the email that the user has just written. This helps to address the relevance effectively by giving preference to the important topics of each culture, and in the recent topics of the mail. This process brings concepts as lunch, food, meal, and salad, that are in the semantic neighborhood of dinner.

Node retrieval (step 3): In this step, we get the tuples whose nodes are in the context of the mail from OMCSNet.MX and OMCSNet.US. The output of this step is the pieces of common sense knowledge for the Mexican and American culture (see Figure 3). At this point we have everything the databases have about the eating habits in both cultures. For example: the output from OMCSNet.MX has the following, among others: ['TakeTime', 'dinner', 'between 8:00 PM and 9:00 PM'], ['KindOf, 'dinner', 'light meal'], ['IsA', 'dessert', 'rice with milk'], ['IsA' 'food' 'chocolate']; and from OMCSNet.US has: ['TakeTime', 'dinner', 'between 6:00 PM and 7:00 PM'], ['KindOf, 'dinner', 'heavy meal'], ['IsA', 'dessert', 'pumpkin pie'], ['IsA' 'food' 'chocolate'].

OMCSNet.BR

''"""'•" OMCSNet.MX

OMCSNet.US -—== cultural

Figure 3. The node retrieval operation.

Relevance of the nodes (step 4): By comparing each node with the cultural sets of knowledge in the previous step we can get its relevance. For this operation, the SIM operation from Cohen's WHIRL [12] is used, as in Figure 4. The interesting part about WHIRL is that it is a system that effectively interleaves inference with retrieval. This operation allows getting the similarity for each node in one set with all the elements of the other set and always maps to a number between zero and one.

Calculation of analogies (step 5): If the value of one node and the semantic relations in the tuples of one set are equal to the tuples of the other cultural set, then the unmatching concept is an analogy between the two cultures that are being considered. In addition, the semantic relation is analyzed in order to avoid irrelevant analogies. These analogies are ranked using the similarity between the two nodes. If

Artificial Intelligence in Theory and Practice !

two different pairs of tuples allow the same analogy, the values of the relevance scores are added. In our example set, the only nodes that are suitable for analogy are the nodes that talk about the 'KindOf meal the dinner is in both cultures. This process is similar to Dedre Centner's classic Structure Mapping analogy method.

> o [^31

Analogies

^O ^;^_____^ Analoeies

Figure 4. Calculation of the relevance and the analogies.

Calculate the nodes to display (step 6): The nodes are sorted using the relevance of their context and their similarities produced by the SIM operator.

Chose the information to display (step 7): The nodes ranked higher are chosen to be displayed. First, we choose the nodes to calculate analogies and then the rest of the nodes, the former nodes give information about things that are different, but do not have a counterpart in the other culture, or are grounding information that makes no sense for an analogy [6].

Map the concept to English (step 8): For each semantic relation in the net, a custom template that maps its information to English sentences was created and applied before displaying the information. For the analogies, an additional template is used to explain why the system made this analogy. In Figure 2 we show only the top four concepts that are displayed after applying the template.

5. Conclusions and Future Works This work has presented some first steps in the modeling and use of cultural

differences in interactive applications. This way, we answer affirmatively to the question proposed in the title of this paper. We model cultural differences by comparing knowledge bases of Commonsense statements collected from volunteer Web contributors in various cultures. We explore applications by those who want to focus on a specific user group; by those who want to develop systems which use the cultural knowledge stored in the knowledge bases; and by those who want to facilitate communication between people, providing mutual knowledge about their cultures. This work is part of a larger effort to model Commonsense knowledge. In [8], we present many applications that have built using the OMCS, ConceptNet, and related tools, for providing intelligent defaults in interactive systems and mapping from user goals to actions. Future work will also include the investigation of cultural expressions in Open Mind Common Sense considering a larger number of facts. Also other domains will be studied in order to verify the cultural differences besides eating habits domain. While we have not yet conducted formal user tests with the


email application, informal feedback from users shows that, while the absolute accuracy of suggestions is not high, users do appreciate the occasional useful suggestion and it is not excessively distracting even when suggestions are not relevant. We hope developers of interactive systems use the knowledge about culture stored in Open Mind Common Sense databases in order to facilitate the interaction between humans and computers.

References 1. Anacleto, J.; Lieberman, H; Tsutsumi, M.; Neris, V.; Carvalho, A.; Espinosa, J.; Zem-

Mascarenhas, S. Using Common Sense to Recognize Cultural Differences. Submitted as a work-in-progress paper to CHI 2006.

2. Bailey, B.P., Gurak, L.J., and Konstan, J.A. An examination of trust production in computer-mediated exchange. Proc. 7th Human Factors and the Web 2001 Conference www.optavia.com/hfweb/7thconferenceproceedings.zip/bailey.pdf Last visited in Jan, 06.

3. Birukov, A., Blanzieri, E, Giorgini, P. Implicit: An AgentBased Recommendation System for Web Search. Proc. AAMAS'05 (July 25-29,2005. Utrecht. Netherlands).

4. Choi, B., Lee, I., Kim, J., Jeon, Y. A Quahtative Cross-National Study of Cultural Influences on Mobile Data Service Design. Proc. CHI 2005.

5. Cohen, William W. WHIRL: A word-based information representation language. Artificial Intelligence (2000) 163-196.

6. Falkenhainer, B., Gentner, D. The Structure-Mapping Engine. Proceedings of the Fifth National Conference on Artificial Intelligence. (1986)

7. Khaslavsky, J. Integrating Culture into Interface Design. Proc CHI 1998 p 365-366.

8. Lieberman, L., Liu, H., Singh, P., Barry, B. Beating Common Sense into Interactive Applications. American Association for Artificial Intelligence, Winter 2005, p 63-76

9. Liu, Hugo. MontyLingua: An End-to-End Natural Language Processor for English. (2003)

10. Liu, Hugo and Singh, Push. OMCSNet: A Commonsense Inference Toolkit. MIT Media Lab Society Of Mind Group Technical Report SOM02-01. (2002) 272-277.

11 .Liu, H.; Singh, P. Commonsense Reasoning in and over Natural Language. Proc. 8th KES 04. http://web.media.mit.edu/~push/CommonsenseInOverNL.pdf Last visited in Jan, 06.

12. Liu, H; Singh P. ConceptNet: A Practical Commonsense Reasoning Toolkit. BT Technology Journal, v. 22, n. 4, p. 211-226, 2004. http://web.media.mit.edu/~push/ConceptNet-BTTJ.pdf Last visited in Jan, 06.

13. Marcus, A. Culture Class vs. Culture Clash. Proc. Interactions 2002 (June. 2002), 25 p.

14. Singh, P. The OpenMind Commonsense Project. KurzweilAI.net, 2002. Available in: :< http://web.media.mit.edu/~push/OMCSProject.pdf>. Last visited in Jan, 06.

15. Spink, A., Ozmutlu S., Ozmutlu H.,Jansen B. J. U.S. VERSUS EUROPEAN WEB SEARCHING TRENDS. SIGIR Forum. Fall 2002, Vol. 36, No. 2, p 32-38

16.Teevan, J., Dumais, S. T., Horvitz, E. Personalizing Search via Automated Analysis of Interests and Activities. Proc. SIGIR '05 (August 15-19, 2005, Salvador, Brazil).

ARTIFICIAL INTELLIGENCE AND KNOWLEDGE MANAGEMENT

Hugo Cesar Hoeschl'; Vania Barcellos^ ^

'WBSA Intelligent Systems S.A.,'Research Institute on e-Gov, Juridical Intelligence and Systems - IJURIS ; ^Federal University of

Santa Catarina - Program of Post-Graduation in Engineering and Knowledge Management

[email protected]; [email protected]

http: //www. ij uri s. or g

Abstract. This article intends to make an analysis about the Artificial Intelligence (AI) and the Knowledge Management (KM). Faced with the dualism mind and body how we be able to see it AI? It doesn't intent to create identical copy of human being, but try to find the better form to represent all the knowledge contained in our minds. The society of the information lives a great paradox, at the same time that we have access to an innumerable amount of information, the capacity and the forms of its processing are very limited. In this context, institutions and centers of research devote themselves to the finding of ways to use to advantage the available data consistently. The interaction of the Knowledge Management with Artificial Intelligence makes possible the development of filtering tools and pre-analysis of the information that appear as a reply to the expectations to extract resulted optimized of databases and open and not-structuralized source, as the Internet.

I. INTRODUCTION

For Castells [1] the technology does not decide to society and neither the society writes the course of the technological transformation, since factors, including creativity and enterprising initiative, intervene in the trial of scientific discovery, technological innovation and social application, so that the final result depends on a complex interactive standard. While the technologies of the information advance, the gears.

Please use the foil owing format when citing this chapter:

Hoeschl, H.C., Barcellos, V., 2006, in TFTP International Federation for Information Processing, Volume 217, Artificial

Intelligence in Theory and Practice, ed. M. Bramer, (Boston; Springer), pp. 11-19.


persons and technologies, go altering their objectives and originating an endless cycle of renewal. Inside that cycle presents-itself of clear form the dizzy evolution of the technology of the data processing, that has amount of studies dedicated to the reproduction of abilities and human capacities such the manuals, as much as the intellectuals, that is the Artificial Intelligence (AI). The intelligence is more than the faculty of learn how, apprehend or understand, interpret and, mainly adapt itself to the situations.

The present study search flow through about the dualism mind and body and the paper of the AI in his incessant search of automation of man (mind and body) (item 2), right away we are going to show an join between AI and Knowledge Management (item 3), showing the present importance of itself to automate the knowledge in the companies. We will show at the end (item 4) the evolution of the studies in AI carried by the Author and his Team and its application.

2. AI AND DUALISM: MIND AND BODY

Suppose a robot in a factory of cars and that we be able to ask it what is his opinion about mind and body. Evidently that creature will not be able to answer promptly, therefore it will have that to inspect before its models, as is programmed for carry out determined functions, for example adapting some piece or performming the painting of the vehicle that is passing over the caterpillar, probably will not obtain no answer.

There have been many discutions about questions of philosophical order and epstemological, questioning any possibility of Artificial Intelligence (AI). It would be possible the construction of an intelligence or similar conscience of human being in a machine? Human intelligence in its biological and animal concepts?

Many authors as John Searle, says that despite a machine could speak Chinese language by resources as examining and comparing data table and binary references this doesn't grant that this machine can really understand and speak the language. It means that whether the machine can realize Turing Tests doesn't grant that it is as conscious as any human being.

The possibility of translating human intelligence to plastic artificial base has a clear limit: If intelligence can be generated from these elements, it must be necessarilly different from human one, because results happen from different human elements.

However they have not been trying to replace human being or to create artificial mind and body, but to replicate special activities and jobs using human being way, as using special robots to save life in a burning , earthshake or anyother place dangerous for human staff get.


Because of discussion of possibility of generate artificial intelligence scientists have being gathering many knowledge since early 50*, these studies have being getting more and more interests because of commercial applications.

Researches in AI are related to areas of application that involve human reasoning, trying to imitate it and performing inferences. As Savory [22] these areas of application that are generally enclosed in the definitions of AI include, among others: intelligent systems or systems based on knowledge; intelligent / learning systems; understanding / translation from natural language; understanding / voice generating; analysis of image, scene in real time and automatic programming.

Notice, then, an introductory and superficial vision, about how artificial intelligence can be defined Pfaffenberger [10]: "Artificial intelligence - The field of the computer sciences that intends to perfect the computers endowing them with some peculiar characteristics of human intelligence, as the capacity to understand natural language and to simulate the reasoning in uncertainty conditions."

The following are important aspects of AI, as Rabuske [11], among others: development of heuristical methods for the solution of problems; representation of knowledge; treatment of natural language; acquisition of knowledge; artificial reasoning and logics and tools. Amongst its main applications, we have the following: mastering systems; processing of natural language; recognition of standards; robotics; intelligent databases; test of theorems and games.

Using of intelligent techniques and trying to develop computer applications provided of logical or structured cases database, to help in the task of the study of facts involves a difficult work.

For Nonaka [7], the cartesian dualism between subject and object or mind and body started from the budget of that the essence of a human being is the rational thoughtful. This thoughtful life seeks the knowledge isolating itself off the remainder of the world and off the others human being. But the imposed contemporary challenges to the cartesian dualism emphasized the importance of some forms of interaction between the self and the external world in the search of the knowledge.

For Choo [2] the needs of information are many times understood like the cognitives necessities of a person: faults or weakness of knowledge or comprehension that can be expressed in questions or topics set to a system or source of information. Then to satisfy cognitive necessity, would be to store the information that answers to what was asked. Then, returning back to our robot in a factory of cars, if a machine in which was installed some system of intelligent search, the answer would be immediate and satisfactory. In that case, the


techniques and models of AI are necessary for an emotional and affectionate search of the humanity that seeks the knowledge.

3. AI AND KNOWLEDGE MANAGEMENT

The Italian sociologist, Domenico di Masi, in his book "O Ocio Criativo", speaks about the war between the companies, in that dispute the concurrent commercially want destroy each other, but when a company is defeated, will not be destroyed, but assimilated. This means, search the patrimony of know-how, of men and of ideas, so that is powerfull to improve the productive units instead of eliminate-her.

The knowledge became to be the focus of the business leaders, that seek way of increasing the performance of its organizations. They will assure the viability and the supported success. Methods and techniques to acquire, represent, share and maintain the knowledge did itself necessary, therefore is in all of the places (software, persons, organizations, nature, among others), and in all the forms, as for example, in a base of facts, in the person, in an organizational practice shared tacitly, or to even in a robot.

In that sense, Choo [2] emphasizes that the creation of extensive knowledge the capacities of the organizations elevating the level of specialization of his own members and learning with persons of outside of his scopes. The same author says that the external and internal ways of creation of knowledge occur in a broader organizational context, defined by an evaluation of the new knowledge regarding the strategic purpose of the organization, an appreciation of his essential capacities, an estimate of the technological potential and of the market, and the recognition that the operational innovations demand the support of new social systems of information.

Like this arose the Knowledge Engineering in late 70th, before sight barely as a discipline of the AI with the objective of creating approaches and tools to build systems based in the knowledge. It researches carried out in that area permitted the knowledge models structures construction, their systematization and, mainly, to their reuse.

In the early the Knowledge Engineering was involved with the art of build specialists systems, systems based in the knowledge and intensive knowledge information systems, summarizing everything, systems based in the knowledge. The systems based in the knowledge are arising from ofthe AI.

For Munoz-Seca [8] despite of intangibility of the knowledge, to be able to handle it physically, needs its transformation in structures stuff. The knowledge must be incorporated to a physical structure that is able


to be transformed by very well established physical methods and from the which can be extracted off new ones by sensorial methods. The knowledge in pure form is not sufficient to satisfy all the needs of the economy. The support for the mind should be supplemented with the support for the body. Consequently, the knowledge should be transformed also in fit entities inside the basic trials of the company and of the society. The materialization of the knowledge should be translated in a form that can be manipulated, stored, transmitted, restrored and used easily, without have of appeal to the person that originated it.

To capture all the knowledge process it, store it and reuse it became the big challenge of the technology that has been finding in the techniques of AI intelligent solutions over of the last decades.

4. RESEARCHES IN AI - EVOLUTION AND APPLICATIONS

We will illustrate the application of AI through some empirical procedures adopted by the author and his team.

The team has a multidisciplinary character, built by researchers with expressive scientific and technical qualification, with formation in distinct areas of the knowledge, such as: Science of the Computation, Right, Administration, Engineering of Output, Systems of Information, Psychology, Science of the Information, and other, graduated as post doctorate, doctorate and master. It produces since 1999 methodologies, software, everybody with techniques and approaches of AI accepted by the national scientific community and international. Of that output, detach the following:

Starting with the methodology CBR - Case Based Reasoning is used in parts with techniques of retrieval of literal information, presenting a superior performance to the traditional data bases. For in such a way, had been developed two new technologies for the team the Structured Contextual Search - SCS and the Dynamically Contextualised Knowledge Representation (DCKR).

CSS® is a methodology that allows the search in natural language through the context of the information contained in the knowledge base, thus breaching, the search paradigm by means of key words and connectors, making it possible for the user to describe a number of characters presented by each consultation, allowing thus, a more elaborated conception of the search. The research is considered ' contextual ' and ' structured ' because of the following reasons: 1. We take into consideration the context of documents stored at the formation


of the rhetorical structure of the system 2. This context guides the process of adjustment of the entrance as well as the comparison and election of documents; 3. At the moment of the elaboration of the consultation, the entrance is not limited to a set of words, or the indication of attributes, being able to assume the format of a question structured by the set of a long text is added to the possibility of operating dynamic weights on specific attributes, that work as ' filters ' and make a preliminary election of documents to be analyzed.

DCKR® consists of a dynamic process of analysis of the general context that involves the problem focused. It makes comparisons between the context of documents, enabling the accomplishment of a more precise search and with a better quality. Moreover, the documents are retrieved through pre-determined indexes, that can be valuated by the user when consulting. This technique implies a significant increment in the performance in knowledge structured systems.

Digesto® - Site of legal search (www.digesto.net), that enables to the user the recuperation of documents regarding doctrine, jurisprudence, legislation and legal articles. It is a tool for searching in the web, that use techniques of databases textuais and DCKR®.

Alpha Themis® - Intelligent software for the retrieval of the knowledge contained in the "resolutions" of the national courts. It is a system of legal technology, one of the first ones in Brazil to unite Artificial Intelligence and Law. It uses techniques of textual Data base and CBR.

Jurisconsulto® - Innovative system to retrieve sentences in computerized data bases through CBR. It uses techniques of textual Data Base and CBR.

Olimpo®- The system has its performance centered in the combination of aspects derived from CBR and from the representation of added literal information to an suitable organization of knowledge the referring to the resolutions of the Security Council of the ONU, what allows the retrieval of texts with characteristics similar to the information supplied by the user in natural language. New documents are automatically enclosed in the knowledge base through the extraction of relevant information through a technique called DCKR®. Concepts of CBR and techniques of information retrieval have been applied for a better performance of the system, resulting in the methodology called

scs®. And the last, the sytem that fusing the Management of the

Knowledge and Artificial Intelligence, called System KMAI. It will be discoursing about the incorporation of this revolutionary model of analysis of information, that it initiates with a methodology called Dynamically Contextualised Knowledge Representation (DCKR)

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