Agents, Infrastructure, Applications and Norms

Michael LuckUniversity of Southampton, UK

Overview Monday

• Agents for next generation computing AgentLink Roadmap

Tuesday• The case for agents• Agent Infrastructure

Conceptual: SMART Technical: Paradigma/actSMART

• Agents and Bioinformatics GeneWeaver myGrid

Wednesday• Norms• Pitfalls

Agent Technology: EnablingNext Generation Computing

A Roadmap for Agent Based Computing

Michael Luck, University of Southampton, UKmml@ecs.soton.ac.uk

Overview

What are agents? AgentLink and the Roadmap Current state-of-the-art Short, medium and long-term

predictions Technical challenges Community challenges Application Opportunities

What is an agent?

A computer system capable of flexible, autonomous (problem-solving) action, situated in dynamic, open, unpredictable and typically multi-agent domains.

What is an agent?

A computer system capable of flexible, autonomous (problem-solving) action, situated in dynamic, open, unpredictable and typically multi-agent domains.

control over internal state and over own behaviour

What is an agent?

A computer system capable of flexible, autonomous (problem-solving) action, situated in dynamic, open, unpredictable and typically multi-agent domains.

experiences environment through sensors and acts through effectors

What is an agent?

A computer system capable of flexible, autonomous (problem-solving) action, situated in dynamic, open, unpredictable and typically multi-agent domains.

reactive: respond in timely fashion to environmental change

proactive: act in anticipation of future goals

Multiple Agents

In most cases, single agent is insufficient

• no such thing as a single agent system (!?)

• multiple agents are the norm, to represent: natural decentralisation multiple loci of control multiple perspectives competing interests

Agent Interactions

Interaction between agents is inevitable• to achieve individual objectives, to manage

inter-dependencies

Conceptualised as taking place at knowledge-level • which goals, at what time, by whom, what for

Flexible run-time initiation and response• cf. design-time, hard-wired nature of extant

approaches

AgentLink and the Roadmap

What is AgentLink?

Open network for agent-based computing.

AgentLink II started in August 2000. Intended to give European industry a

head start in a crucial new area of IT. Builds on existing activities from

AgentLink (1998-2000)

AgentLink Goals

Competitive advantage through promotion of agent systems technology

Improvement in standard, profile, industrial relevance of research in agents

Promote excellence of teaching and training

High quality forum for R&D

What does AgentLink do?

Industry action• gaining advantage for Euro industry

Research coordination• excellence & relevance of Euro research

Education & training• fostering agent skills

Special Interest Groups• focused interactions

Information infrastructrure• facilitating AgentLink work

The Roadmap: Aims

A key deliverable of AgentLink II Derives from work of AgentLink SIGs Draws on Industry and Research

workpackages Aimed at policy-makers, funding

agencies, academics, industrialists Aims to focus future R&D efforts

Special Interest Groups

Agent-Mediated Electronic Commerce Agent-Based Social Simulation Methodologies and Software Engineering

for Agent Systems Intelligent Information Agents Intelligent and Mobile Agents for

Telecoms and the Internet Agents that Learn, Adapt and Discover Logic and Agents

The Roadmap: Process

Core roadmapping team:• Michael Luck• Peter McBurney• Chris Preist

Inputs from SIGs: area roadmaps Specific reviews Wide consultation exercise Collation and integration

State of the art

Views of Agents

To support next generation computing through facilitating agent technologies

As a metaphor for the design of complex, distributed computational systems

As a source of technologies As simulation models of complex real-

world systems, such as in biology and economics

Agents as Design

Agent oriented software engineering

Agent architectures Mobile agents Agent infrastructure Electronic institutions

Agent technologies

Multi-agent planning Agent communication languages Coordination mechanisms Matchmaking architectures Information agents and basic ontologies Auction mechanism design Negotiation strategies Learning

Links to other disciplines

Philosophy Logic Economics Social sciences Biology

Application and Deployment Assistant agents Multi-agent decision systems Multi-agent simulation systems

IBM, HP Labs, Siemens, Motorola, BT Lost Wax, Agent Oriented Software,

Whitestein, Living Systems, iSOCO

The Roadmap Timeline

Dimensions

Sharing of knowledge and goals Design by same or diverse teams Languages and interaction

protocols Scale of agents, users, complexity Design methodologies

Current situation

One design team Agents sharing common goals Closed agent systems applied in

specific environment Ad-hoc designs Predefined communications

protocols and languages Scalability only in simulation

Short term to 2005

Fewer common goals Use of semi-structured agent

communication languages (such as FIPA ACL)

Top-down design methodologies such as GAIA

Scalability extended to predetermined and domain-specific environments

Medium term 2006-2008

Design by different teams Use of agreed protocols and languages Standard, agent-specific design methodologies Open agent systems in specific domains (such

as in bioinformatics and e-commerce) More general scalability, arbitrary numbers

and diversity of agents in each such domain Bridging agents translating between domains

Long Term 2009-

Design by diverse teams Truly-open and fully-scalable multi-

agent systems Across domains Agents capable of learning appropriate

communications protocols upon entry to a system

Protocols emerging and evolving through actual agent interactions.

The Roadmap Timeline

Technological Challenges

Technological Challenges

Increase quality of agent systems to industrial standard

Provide effective agreed standards to allow open systems development

Provide infrastructure for open agent communities

Develop reasoning capabilities for agents in open environments

Technological Challenges

Develop agent ability to adapt to changes in environment

Develop agent ability to understand user requirements

Ensure user confidence and trust in agents

Industrial Strength Software Fundamental obstacle to take-up is lack

of mature software methodology• Coordination, interaction, organisation,

society - joint goals, plans, norms, protocols, etc

• Libraries of … agent and organisation models communication languages and patterns ontology patterns

CASE tools AUML is one example

Industrial Strength Software

Agreed Standards

FIPA and OMG• Agent platform architectures• Semantic communication and content

languages for messages and protocols

• Interoperability• Ontology modelling

Public libraries in other areas will be required

Agreed Standards

Semantic Infrastructure for Open Communities Need to understand relation of agents,

databases and information systems Real world implications of information

agents Benchmarks for performance Use new web standards for structural

and semantic description Services that make use of such semantic

representations

Semantic Infrastructure for Open Communities Ontologies

• DAML+OIL• UML• OWL

Timely covergence of technologies Generic tool and service support Shared ontologies Semantic Web community exploring

many questions

Semantic Infrastructure for Open Communities

Reasoning in Open Environments Cannot handle issues inherent in

open multi-agent systems• Heterogeneity• Trust and accountability• Failure handling and recovery• Societal change

Domain-specific models of reasoning

Reasoning in Open Environments Coalition formation Dynamic establishment of virtual

organisations Demanded by emerging

computational infrastructure such as• Grid• Web Services• eBusiness workflow systems

Reasoning in Open Environments Negotiation and argumentation

• Some existing work but currently in infancy

Need to address• Rigorous testing in realistic environments• Overarching theory or methodology• Efficient argumentation engines• Techniques for user preference specification• Techniques for user creation and dissolution

of virtual organisations

Reasoning in Open Environments

Learning Technologies

Ability to understand user requirements• Integration of machine learning• XML profiles

Ability to adapt to changes in environment• Multi-agent learning is far behind single agent

learning• Personal information management raises

issues of privacy

Relationship to Semantic Web

Learning Technologies

Trust and Reputation

User confidence Trust of users in agents

• Issues of autonomy• Formal methods and verification

Trust of agents in agents• Norms• Reputation• Contracts

Trust and Reputation

Challenges for the Agent Community

Community Organisation

Leverage underpinning work on similar problems in Computer Science: Object technology, software engineering, distributed systems

Link with related areas in Computer Science dealing with different problems: Artificial life, uncertainty in AI, mathematical modelling

Community Organisation

Extend and deepen links with other disciplines: Economics, logic, philosophy, sociology, etc

Encourage industry take-up: Prototypes, early adopters, case-studies, best practice, early training

Existing software technology Build bridges with distributed systems,

software engineering and object technology.

Develop agent tools and technologies on existing standards.

Engage in related (lower level) standardisation activities (UDDI, WSDL, WSFL, XLANG, OMG CORBA).

Clarify relationships between agent theories and abstract theories of distributed computation.

Different problems from related areas Build bridges to artificial life,

robotics, Uncertainty in AI, logic programming and traditional mathematical modelling.

Develop agent-based systems using hybrid approaches.

Develop metrics to assess relative strengths and weakness of different approaches.

Prior results from other disciplines Maintain and deepen links with

economics, game theory, logic, philosophy and biology.

Build new connections with sociology, anthropology, organisation design, political science, marketing theory and decision theory.

Encourage agent deployment Build prototypes spanning organisational

boundaries (potentially conflicting). Encourage early adopters of agent

technology, especially ones with some risk.

Develop catalogue of early adopter case studies, both successful and unsuccessful.

Provide analyses of reasons for success and failure cases.

Encourage agent deployment Identify best practice for agent

oriented development and deployment.

Support standardisation efforts. Support early industry training efforts. Provide migration paths to allow

smooth evolution of agent-based solutions, from today’s solutions,

Application Opportunities

Application Opportunities

Ambient Intelligence Bioinformatics and Computational

Biology Grid Computing Electronic Business Simulation Semantic Web

Ambient Intelligence

Pillar of European Commission’s IST vision Also developed by Philips in long-term

vision Three parts

• Ubiquitous computing• Ubiquitous communication• Intelligent user interfaces

Thousands on mobile and embedded devices interacting to support user-centred goals and activity

Ambient Intelligence

Suggests a component-oriented world populated by agents• Autonomy• Distribution• Adaptation• Responsiveness

Demands• Virtual organisations• Infrastructure• Scalability

Bioinformatics

Information explosion in genomics and proteomics

Distributed resources include databases and analysis tools

Demands automated information gathering and inference tools

Open, dynamic and heterogeneous Examples: Geneweaver, myGrid

Grid Computing

Support for large scale scientific endeavour

More general applications with large scale information handling, knowledge management, service provision

Suggests virtual organisations and agents

Future model for service-oriented environments

Electronic Business

Agents currently used in first stage – merchant discovery and brokering

Next step is real trading – negotiating deals and making purchases

Potential impact on the supply chain Rise in agent-mediated auctions

expected• Agents recommend• But agents do not yet authorise agreements

Electronic Business

Short term: travel agents, etc• TAC is a driver

Long term: full supply chain integration

At start of 2001, there were• 1000 public eMarkets• 30,000 private exchange

Simulation

Education and training Scenario exploration Entertainment

The Two Towers

Thousands of agents simulated using the MASSIVE system

Realistic behaviour for battle scenes

Initial versions included characters running away!

Previous use of computational characters did not use agent behaviour (eg Titanic).

Current State

Pivotal role in contributing to broader visions of Ambient Intelligence, Grid Computing, Semantic Web, etc.

European strength is broad and deep Still requires integration, needs to avoid

fragmentation, needs effective coordination

Needs to support industry take-up and innovation

For more information ...

Dr Michael Luck Department of Electronics and

Computer ScienceUniversity of SouthamptonSouthampton SO17 1BJUnited Kingdom

Feedback sought: please send feedback!

Roadmap: www.agentlink.org/roadmap

The Book

The CD

The Agent Portalwww.agentlink.org