Virtual Agent Modeling in the RASCALLI Platform

(Special Session on EU-projects)

Christian EisResearch Studios Austria

Vienna, Austriachristian.eis@

researchstudio.at

Marcin SkowronAustrian Research Institute for

Artificial IntelligenceVienna, Austria

marcin.skowron@ofai.at

Brigitte KrennAustrian Research Institute for

Artificial Intelligence,Research Studios Austria

Vienna, Austriabrigitte.krenn@ofai.at

ABSTRACTThe RASCALLI platform is both a runtime and a develop-ment environment for virtual systems augmented with cog-nition. It provides a framework for the implementation andexecution of modular software agents. Due to the underly-ing software architecture and the modularity of the agents,it allows the parallel execution and evaluation of multipleagents. These agents might be all of the same kind or ofvastly different kinds or they might differ only in specific(cognitive) aspects, so that the performance of these aspectscan be effectively compared and evaluated.

Categories and Subject DescriptorsI.2.11 [Distributed Artificial Intelligence]: Intelligentagents; D.2.11 [Software Architectures]: Domain-specificarchitectures; K.6.3 [Software Management]: Softwaredevelopment

General TermsDesign, Experimentation, Measurement

KeywordsCognitive agents, Agent modeling, Agent evaluation, Opensource software

1. INTRODUCTIONThis paper gives an overview of the architecture and func-

tionality of the RASCALLI platform, developed as part ofthe RASCALLI project1. In this project, the platform isused as the underlying software environment for the devel-opment and execution of so called RASCALLI (ResponsiveArtificial Situated Cognitive Agents that Live and Learn onthe Internet). It provides the facilities for user-agent and

1European Commission Cognitive Systems Project FP6-IST-027596-2004 RASCALLI.

c©ACM, 2008. This is the author’s version of the work. It is posted hereby permission of ACM for your personal use. Not for redistribution. Thedefinitive version was published in the proceedings of PerMIS’08, August19–21, 2008, Gaithersburg, MD, USA.http://doi.acm.org/10.1145/nnnnnn.nnnnnn

agent-agent communication and serves as a testbed for theevaluation of various incarnations of the agents that use dif-ferent sets of action-perception tools, action selection mech-anisms and knowledge resources. The platform supportsa modular development style, where agents are assembledfrom small re-usable building blocks. Agents of differentkinds and configurations can run simultaneously within asingle platform environment. This enables the evaluationand comparison of different agents as well as the evaluationof whole agent communities.

The paper is organized as follows: Section 2 describes thegeneral objectives of the RASCALLI project to the extentthat they are relevant for the design and implementation ofthe RASCALLI platform. Section 3 gives a brief overviewof related platforms and methodologies. The RASCALLIplatform itself is then described in section 4, and section 5gives an overview of the agent components implemented inthe RASCALLI project. Finally, section 6 explains how theRASCALLI platform can be used for agent evaluation.

2. PROJECT OBJECTIVESThe project RASCALLI aims at the development of vir-

tual agents that perform tasks related to accessing and pro-cessing information from the Internet and domain-specificknowledge bases. RASCALLI agents, further referred toas Rascalli, represent a growing class of cooperative agentsthat do not have a physical presence, but nevertheless areequipped with major ingredients of cognition including sit-uated correlates of physical embodiment to become adap-tive, cooperative and self improving in a virtual environ-ment, given certain tasks.

The project objectives cover the following topics: devel-opment of a computational framework for the realizationof cognitive agents providing intelligent assistance capabil-ities; cognitive architecture and modeling; perception andaction; reasoning; learning; communication; agent-to-agentand agent-to-user interfaces. The Rascalli answer questionsof their users, learn the users’ preferences and interests, anduse this knowledge to present the users with new, interest-ing information. The agents exist in an environment con-sisting of external knowledge sources on the Internet, suchas search engines and RSS feeds, and domain-specific knowl-edge bases. They communicate with their users as well aswith other Rascalli.

Rascalli are developed in a modular fashion, which allowsindividual agents to be built from different sets of compo-nents. This enables e.g. the creation of agents which are“ex-

perts” in different knowledge domains. During its life-time,each agent adapts to its user’s interests and thus further spe-cializes in a certain sub-domain and evolves in accordancewith the user’s preferences. This results in a community ofspecialized agents, which can communicate with each otherto provide requested information to their users.

The modular approach enables the evaluation and com-parison of different sets of components, including differentcognitive aspects (e.g. different learning strategies) by exe-cuting multiple Rascalli in the same environment and eval-uating their performance (e.g. by means of user tests).

For the realization of these objectives, system integrationturned out to be a major roadblock, due to the followingreasons:

• Even though Java was chosen as the main implemen-tation language for the project, some project partnershave no or little experience with Java development.

• In order to avoid re-implementation, we had to in-tegrate existing components from previous projects.These components are based on a wide range of tech-nologies, such as different programming languages (e.g.Perl, Java, Lisp) and even native binaries for differentoperating sytems (Linux and Windows).

• Initial attempts at providing a development environ-ment that integrates all of these components and canbe replicated on each developer’s machine proved tobe difficult to use and keep up-to-date.

Based on the project objectives and constraints outlinedabove, we arrived at the following set of requirements forour software platform:

• Support the execution of various agents, belonging todifferent users.

• Support agent-to-agent and agent-to-user communica-tion.

• Allow developers to implement diverse agents based onshared components (this also means that multiple ver-sions of each component can exist at the same time).

• Integrate external and legacy components with mini-mal effort.

• Build agents in a modular, component-based fashion.

• Build the platform itself in a component-based, exten-sible fashion.

3. RELATED WORKResearch into related work has been conducted in mul-

tiple directions, including multi-agent platforms, as well asplatforms and development methodologies for cognitive sys-tems. While many such platforms and methodologies exist,none of them meets the requirements set for the RASCALLIplatform.

3.1 Multi-Agent PlatformsWe have investigated FIPA2 compliant agent platforms,

such as JADE3 [1], which is a Java-based middleware formulti-agent systems. However, these systems mostly focuson distributed systems and communication issues. Also, theRASCALLI platform is not a multi-agent system in the tra-ditional sense, where agents are independent componentsof a larger application, working for a common goal. In-stead, Rascalli are complete individual entities that simplyhappen to share the same environment and may communi-cate with each other, if they wish. Furthermore, none ofthe investigated agent platforms supports the developmentstyle targeted by the RASCALLI platform, where multipleagent architectures and agent definitions, as well as multipleversions of agent components co-exist in a single platforminstance.

It might be interesting future work to implement or inte-grate some of the FIPA standards (such as the Agent Com-munication Language) with the RASCALLI platform.

3.2 Agent Development Methodologies andFrameworks

As an example of an agent development methodology, Be-havior Oriented Design (BOD)4 [2] supports the implemen-tation of agents based on an iterative development processand a modular design. However, it does not provide muchof a runtime environment. Therefore, BOD does not re-ally compare to the RASCALLI platform, even though bothadvocate a modular approach. It would, however, be inter-esting to implement an agent architecture based on BODwithin the RASCALLI platform and thus use the platformas a runtime environment for BOD agents.

As for development frameworks, AKIRA5 [7] aims to cre-ate a C++ development framework to build cognitive archi-tectures and complex artificial intelligent agents. However,like the FIPA multi-agent platforms, it targets different re-quirements than the RASCALLI platform. It might be in-teresting to consider exploiting some of AKIRA’s conceptswithin the RASCALLI platform.

4. RASCALLI PLATFORMThis section provides an overview of the RASCALLI plat-

form. We start with a set of features supported by the plat-form in order to fulfill the requirements listed above. Thenwe describe the software architecture of the platform andexplain how this architecture supports the various platformfeatures.

4.1 Platform Features

Multi-Agent: The platform supports the concurrent exe-cution of multiple agents, including agents of the samekind, as well as agent of different kinds, ranging fromvery similar to vastly different.

Multi-User: Each agent has a single user (but one usermay have several agents).

2http://www.fipa.org/3http://jade.tilab.com/4http://www.cs.bath.ac.uk/ai/AmonI-sw.html5https://sourceforge.net/projects/a-k-i-r-a/

Figure 1: Platform Layers

Communication: Agent-to-agent communication can beimplemented on the Java level, since all agents runwithin a single runtime environment. Alternatively,agents can communicate via instant messaging (thisopens the possibility to use multiple, distributed plat-form instances). Several channels for agent-to-usercommunication have been implemented (currently aproprietary protocol for the 3D client interface, Jab-ber instant messaging and email).

Component-Based Architecture: All the parts that arerequired to set up a specific agent are developed in acomponent-based fashion so that individual Rascallican be assembled from these components in a Lego-like manner.

Extensibility: The platform itself is built in a component-based fashion and can therefore be easily extended,even at runtime.

Distributed, Concurrent Development: A single plat-form instance is shared by all developers for imple-menting different agents, with minimal interference be-tween individual developers.

Multi-Version: In order to support the concurrent devel-opment of different kinds of agents based on sharedcomponents, the platform supports the execution ofmultiple versions of the same components at the sametime.

System Integration: External (and possibly legacy) com-ponents need to be integrated only once and are thenavailable to all agents running in the platform in aneasy-to-use manner. Since only one instance of theplatform is required, there is no need to duplicate theentire software environment on multiple developer ma-chines.

4.2 Platform ArchitectureThe RASCALLI platform is implemented as a layered ar-

chitecture (Fig. 1).

4.2.1 Infrastructure LayerThe Infrastructure Layer contains basic tools and compo-

nents used in the RASCALLI project. Specifically, these areJava, Maven6 and OSGi7. In addition, this layer containscustom-made development and administration tools for theRASCALLI platform, such as user interfaces for agent con-figuration and deployment tools.

The most important feature of this layer is the use ofOSGi, which implements a dynamic component model ontop of Java. This means that components can be installed,

6http://maven.apache.org/7http://www.osgi.org/

Figure 2: Agent Layer

started, stopped and uninstalled at runtime. Furthermore,dependencies between components are managed by OSGi ina fashion that allows the execution of multiple versions of asingle component at the same time. Finally, OSGi provides aframework for service-based architectures, where each com-ponent can provide services to other components, based onJava interface specifications.

The use of OSGi thus enables the platform features ’multi-version’ and ’extensibility’, and supports the implementa-tion of a ’component-based architecture’ in the upper twoplatform layers.

’Distributed, concurrent development’ is enabled by Ma-ven and some custom-made components on this layer.

4.2.2 Framework LayerThe Framework Layer comprises general platform services

and utilities employed by the Rascalli, including communi-cation (user to agent, agent to agent), event handling, RDFhandling, technology integration (Perl, web services, etc.),and various other platform services.

The services on this layer implement the ’multi-agent’,’multi-user’ and ’communication’ features of the platform.Furthermore, this is the place where ’system integration’takes place. External components are integrated and madeavailable to the components of the Agent Layer as OSGiservices, which can then be accessed on the Java level.

4.2.3 Agent LayerThe Agent Layer is the application layer of the platform

and contains the implementation of the actual agents. Itis designed to support the development and execution ofmultiple agents of different kinds as required by the projectobjectives. This layer consists of the following sub-layers:

Agent Architecture Layer: An agent architecture is ablueprint defining the architectural core of a partic-ular type of Rascalli. More precisely, it sets the rolesof agent components and provides means for definingand assembling a specific agent. The architecture canalso contain implementations of common componentsshared by all agent definitions.

Agent Component Layer: Contains implementations ofthe roles defined on the Agent Architecture Layer.

Agent Definition Layer: An agent definition is an assem-bly of specific components of the Agent Component

Figure 3: MBE agent architecture

Layer of a specific agent architecture. Different agentdefinitions for the same agent architecture might con-tain different components for certain roles.

Agent Instance Layer: Contains the individual agent in-stances. Each Rascallo is an instantiation of a specificagent definition.

Fig. 2 gives a (simplified) example of an agent architec-ture (the Mind-Body-Environment architecture, see section5). The Agent Architecture Layer defines two roles, Mindand Tool, and implements an agent component (Action Dis-patcher) shared by all agent definitions. The Agent Compo-nent Layer contains two implementations of the Mind role,as well as two Tools. Based on this architecture, two agentdefinitions combine each of the Mind implementations withthe available Tools and the Action Dispatcher into differentkinds of agents. Finally, a number of agent instances areshown on the Agent Instance Layer.

5. AGENT COMPONENTSThis section gives an overview of the agent components

which have been implemented in the RASCALLI project(for more detailed information see [10]). RASCALLI agentsare currently based on a reactive agent architecture witha perception-action loop. This agent architecture is calledMind-Body-Environment architecture, because each agenthas a central control unit (Mind), which perceives and actson the Environment via a set of sensor and effector tools(Body), as shown in Fig. 3.

The Environment comprises the agent’s user, other agents,Internet services and domain-specific knowledge bases. Theagent perceives its environment (via a set of perception sen-sors, implemented as software tools) as a set of virtual en-tities with their own characteristics and properties. Theseinclude strings of written language originating from the useror extracted from HTML documents, markup tags, infor-mation about the accessibility of various Internet and localtools and resources, user feedback, etc. Therefore, the agenthas to deal with a dynamic environment i.e. evolving con-tent of the websites, permanent or temporary inaccessibilityof Internet services, appearance of new content or services,changes in the user preferences and interests, as well as thenatural language input from the user and the web-pages.Similarly, all actions of an agent in its environment are per-formed on the above introduced set of entities.

The Body contains components called Tools, which serveas sensors (Perception Layer) and effectors (Actuator Layer).A specific agent definition may contain an arbitrary subsetof the available Tools, but of course, the chosen Mind com-ponent must be able to deal with the selected Tools. Someof the available Tools are:

• T-IP4Dual, T-IP4Simple and T-IP4Adaptive,Input Processing Tools transform natural language anduser feedback inputs into categorized information use-able by the respective Mind components, and serve asa Perception Layer of an agent.

• T-MMG, for generating multi-modal output to theuser. The Multi-Modal Generation Component pro-vides a middle-ware functionality between generatedagent output and the user interfaces. The generationcomponent implements a template-based approach (inform of Velocity templates) by encoding vocabulary,phrases, gestures etc., which can be combined withthe output of the RASCALLI Tools and context data.The use of Velocity8, a template generation engine, al-lows templates to be designed and refined separatelyfrom the application code.

• T-QA, a general purpose open-domain question an-swering system (based on the work described in [8],[9]) is used in the RASCALLI platform to provide an-swers to the user factoid-type questions expressed innatural language.

• T-Nalqi, a natural language database query interface.The Tool is used in the RASCALLI platform for query-ing the databases accessible to the Rascalli, in a searchfor instances and concepts that can provide answers tothe user questions. The component analyses naturallanguage questions posed by the user and retrieves an-swers from the system’s domain-specific databases.

• T-RSS, provides a mechanism for Rascalli to retrievecurrent information that might be of interest for theuser.

• T-Wikipedia, an interface and analysis Tool for Wiki-pedia.

The Mind component performs action selection, based onthe current input from the environment and the agent in-ternal state. It can also make use of supporting services, forexample a user/agent modeling service. The following Mindcomponents are being developed in the RASCALLI project:

• Simple Mind, which performs action selection witha simple rule-based mechanism. These rules match tospecific cues in the input data arriving from sensorchannels. ’Simple Mind’ extracts relevant informationand passes this information on to the appropriate effec-tor tool. Even though seemingly non-trivial behaviorcan be accomplished through a series of interactions ofthe Simple Mind and the available tools, the SimpleMind does not contain any cognitive aspects such asmemory or learning.

8http://velocity.apache.org

• DUAL Mind, which incorporates the DUAL/AMBR([5], [6]) cognitive architecture for action selection. Itincludes a long term memory (LTM) where generaland episodic knowledge is stored, and a working mem-ory (WM) constituted by the active part of LTM, per-ceptual input and goals. The DUAL mind operatesonly on represented knowledge and has only a me-diated connection to the body and the environment.Thus it contains a partial, selected representation ofthe environment at an abstract conceptual level andexperiential memories related to specific episodes likeorganization of the interaction of an agent with its en-vironment.

• Adaptive Mind, a machine learning based classifi-cation driven action selection mechanism. Based onthe available knowledge, including the perception ofan input situation, the agent finds a set of actionsthat can be applied to a given input situation. Theselection of a particular action is based on the simi-larities with other actions the agent had successfullyperformed in the past, i.e. received positive feedbackfrom the user. The action selection classifiers are im-plemented as Maximum Entropy models [4]. The dataused for training the classifiers represents an input sit-uation in terms of entities from the environment per-ceived by the agent, an action applied to this situationand the feedback obtained from the user.

RASCALLI agents come with a variety of user interfacescomprising a 3D client featuring an embodied conversationalcharacter [3] (ECA-UI, see Fig. 4 and 5), a Jabber instantmessaging integration, a web-based user interface (Web-UI)and two domain-specific, special purpose interfaces whichallow the user the explore in a playful way the domain-specific knowledgebases accessible to the currently imple-mented Rascalli. The Web-UI9 mainly serves for user reg-istration, download of the 3D-client and specification of In-ternet resources (URLs and RSS-feeds) that are consideredby the user as important to be monitored by the agent.The agents use the Web-UI to present list-like informationto the user, and more generally all information not wellsuited for presentation by means of synthesized speech. TheECA-UI, on the contrary, specializes on virtual human-to-human dialogue. In order to avoid the bottelneck imposedby speech recognition, user input is restrained to utterancestyped into a small text window and to pressing buttons inorder to praise or scold the agent. The user is expectedto ask domain-specific questions but may also engage in achatterbot-type of conversation with the ECA. To do thisthe Rascalli make use of ALICE chat bot technology.10

The knowledge sources are a music database featuringsongs and albums of more than 60,000 singers/musiciansand a database providing background information on mu-sical artists such as their family relations, religion, trackrecord, band membership etc.11

Based on these components, multiple agent definitionshave been conceptualized and developed, including the ini-tial implementation of an agent with a basic set of tools

9http://intralife.researchstudio.at/rascalli/10http://www.alicebot.org/downloads/programs.html11See http://rascalli.researchstudio.at/ andhttp://rascalli.dfki.de/ontology/ for browsing the data.

(sandbox for testing the system components and their inte-gration), an agent utilizing an implementation of the DUALcognitive architecture as central control unit, and a SmartMusic Companion. Using those various incarnations of theagents implemented in the RASCALLI platform the perfor-mance (in terms of user satisfaction) can be compared andevaluated.

6. AGENT EVALUATIONIn the following, we first give an overview of evaluation

scenarios supported by the RASCALLI platform in generaland the currently implemented Rascalli in particular. Wethen give a more detailed account of an evaluation scenariothe goal of which is to investigate how the use of cognitiveaspects in an agent can improve the user experience.

6.1 Evaluation ScenariosWe distinguish three kinds of scenarios:

1. Automated performance measures: The high modular-ity of the platform eases the integration of automatedperformance measures with existing and future agentarchitectures. For example, one could easily measurethe time an agent needs to fulfill a given task, as wellas the accuracy with which an agent performs certaintasks.

2. Data mining from user activity logs: All user activitiesare logged in the platform including user id, agent id,timestamp and activity type. These data can then beevaluated employing data mining techniques and otherquantitative evaluation methods, in order to identifyprevalent usage patterns of individual users and acrossdifferent users, as well as different kinds of agents.

3. User testing: We distinguish two kinds of user test-ings. The one are studies where users interact withan agent for a short time to fulfill a certain, narrowlydefined task. The other one are studies where usersinteract with their agents more freely for a longer pe-riod of time. While with the former the focus lies ontesting specific aspects of the system and the relatedhuman-computer interaction, the latter address moregeneral questions about what makes an agent a suit-able companion for its user, which interfaces supportwhich tasks and how the interaction of the user withthe agent and his/her attitude towards, his/her lik-ing and understanding of the companion changes overtime.

As there are currently no automated performance mea-sures built into the RASCALLI platform, and the platformhas not yet been made accessible to a broader public andthus we still lack suffient amounts of usage data for applyingdata mining techniques, we will concentrate in the followingon user testing. Due to its modularity, the platform allowsus to experiment with agents being placed in the same en-vironment, but assembled from different internal buildingblocks and therefore are equipped with different perceptionand action capabilities as well as decision making strategies.

A male (Fig. 4) and a female (Fig. 5), human-like versionof the 3D character have been implemented. Both charac-ters are built on the basis of the same body model and areequipped with a similar set of gestures and facial expressions.

Figure 4: ECA user interface (male)

Figure 5: ECA user interface (female)

They are also comparable as regards their appearance, bothare designed to fit a pop-rock scenario. The identity of thecharacters except for gender related optical features allowsus to study effects that may be attributed to different per-ception of gender by simply switching between the male andfemale character whereas all other parameters in the sys-tem are kept the same. The RASCALLI system enables us,without further programming, to experiment with all kindsof settings where we let a user interact with or train his/heragent making use of either the male or the female version ofthe 3D character and then switch to the opposite sex.

The availability of two conceptually different, complemen-tary UIs for user-agent communication, i.e., the Web-UI forstandard windows and menu-based human-computer inter-action and the ECA-UI for interaction closely related to hu-man multimodal dialogue, is a valuable prerequisite to in-vestigate user preferences for different modes of interactionand social implications of human-computer interfaces.

At the time of writing two user studies are completed: theone (S1) concerning the liking and evaluation of individualusers engaging in a prescripted question-answer communi-cation with the male Rascalli character, the other one (S2)is a study concerning the usability of the interface to themusic data. Two further user studies are under prepara-tion. The one (S3) is a replication of the question-answer

study employing the female character instead of the maleone. Whereas the previously mentioned studies are all one-time encounters with the system, the second one (S4) ofthe studies under preparation is a longer-term study wherethe users interact with and train their agents over a longerperiod of time, with user assessments at the beginning, atseveral intermediate stages and at the end of the agent-usercollaboration period. Amongst others, we are interested inchanges over time of the users’ liking of their agents, theirexpectations on the performance of their agents, their ac-counts of trust, and their strategies to adapt to their agentsin order to achieve/satisfy their information requirements incollaboration with their agents.

In particular, we prepare two variants (narrow and broad)of the longer-term study. In the narrow study (S4.1), thetask is to train the agent as good as possible to monitorRSS-feeds for a specific topic of interest. To do so, the usersare asked to identifiy and sharpen a topic of interest suchas artist, group, genre, song, album etc. by exploring themusic-related knowledge of their agent utilizing the agent’sdomain-specific interfaces. In addition, they are asked toinform the agent about preferred Internet sources by speci-fying respective RSS feeds through the Web-UI. The agentwill then monitor the feeds and alert the user when encoun-tering relevant information making use of a Jabber client.

In the broad study (S4.2) the users are left much more un-guided. Other than in S4.1 where the users are confined tothe two domain-specific interfaces and the Web-UI, in S4.2they have access to all UIs. The users are more generallyinformed that the agents have some domian-specific knowl-edge about popular music and besides can access the Inter-net. Depending on what information the users frequentlyaccess, which Internet sources they specify, and which feed-back (praise, scolding) they give to their agents, the agentswill adapt to the users’ interests and aim at providing morerelated information. The more effort the users invest intraining their agents, the better the agents should becomein providing the users with new information relevant to theusers’ interests.

6.2 Evaluation Example: Use of Cognitive As-pects in an RSS Feed Filtering Scenario

We make use of a simple scenario for RSS feed filtering toillustrate the platform’s capability to augment the agents’abilities by incorporating cognitive aspects into processing.Since the different agents can exist at the same time in thesame environment and thus be subject to the same externalinfluences, they can be reliably compared and evaluated.

Recall: In the RSS Feed Filtering scenario, the agents’task is to provide their users with (potentially) interestinginformation gathered from music-related RSS feeds on theInternet. The users train their agents according to theirinterests, thus creating an agent profile containing relevantkeywords such as artist names, song names and genre names.The users also provide RSS feed URLs to their agents, whichthe agents then query for new information.

The research question for this simple scenario is whethercertain properties of an agent improve the overall user satis-faction (as measured by the rate of false positives and neg-atives). Therefore, in addition to the basic keyword-basedfiltering of RSS feeds, the following elements can be addedto a particular agent:

• The ability to find similar agents (agents with a similar

agent profile) and consider those agents’ RSS feeds inaddition to the ones provided by its own user. Thisessentially extends the agent’s search space.

• The ability to find items (artists, songs, etc.) similarto the ones specified in the agent profile. These itemsare then added to the filter keywords, thus extendingthe agent’s search criteria.

The services required to perform these tasks (finding sim-ilar agents, finding similar artists, etc.) are provided by theRASCALLI framework and made available to the agents asadditional Tools in the action and perception layers.

This setup allows us to create and compare four agent def-initions, containing neither of the two features, one of them,or both. For the evaluation, different users are equippedwith one of the available types of agents. The users canthen identify false positives and negatives.

Obviously, this example could be implemented without acomplex system such as the RASCALLI platform. However,even in this simple case the platform has some advantagesto offer:

• Components can be shared between agents. For exam-ple, the service to find similar agents is an external webservice, which is made available within the platform asa simple OSGi/Java service.

• The platform provides a single runtime environmentfor all the agents (there would be multiple agents ofeach kind), so that inter-agent communication (for ex-changing RSS feed URLs) can be easily implemented.

7. CONCLUSIONSThe RASCALLI platform meets a unique set of require-

ments, that is not targeted by any of the investigated plat-forms or methodologies. In particular, it supports the devel-opment and execution of multiple agents of different kinds.Furthermore, it supports the evaluation and comparison ofsuch different agents within a single environment.

Future work includes the completion of the developmentenvironment (e.g. the integration with development toolssuch as the Eclipse IDE), as well as the possible integrationof certain aspects of other projects, such as BOD, AKIRAor JADE.

The RASCALLI platform and selected system compo-nents will be made available to the research community bythe end of 2008 via the project homepage.12 In particu-lar, the project partners Research Studios Austria (SAT)and the Austrian Research Institute for Artificial Intelli-gence (OFAI) will provide an open source version of theplatform and Tools, respectively. In addition, SAT will makeavailable their other system components and user interfacesas managed services with limited support and data volumefree of charge for the research community. The German Re-search Center for Artificial Intelligence (DFKI) will makeavailable their data sources via an open research license andprovide their system components as webservices. The com-panies Ontotext and Radon Labs will make available theirSwiftOWLIM semantic repository and the 3D client, respec-tively. The New Bulgarian University (NBU) will conributetheir DUAL-inspired implementation of an agent’s mind.

12http://www.ofai.at/rascalli

8. ACKNOWLEDGMENTSThis research is supported by the EC Cognitive Systems

Project FP6-IST-027596-2004 RASCALLI, by the nationalFFG program ’Advanced Knowledge Technologies: Ground-ing, Fusion, Applications’ (project SELP), and by the Fed-eral Ministry of Economics and Labour of the Republic ofAustria. The work presented here builds on joint workwithin the RASCALLI project. In particular the authorswould like to thank our collegues from the following institu-tions: SAT, OFAI, Radon, NBU, Ontotext and DFKI. Fi-nally, our participation in the PerMIS’08 workshop is sup-ported by the European Network for the Advancement ofArtificial Cognitive Systems (euCognition, European Com-mission, Unit E5 - Cognition, FP6 Project 26408).

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