Agents, Infrastructure, Applications and Norms

1. Agents, Infrastructure, Applications and Norms Michael Luck University of Southampton, UK

2. 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

3. Agent Technology: EnablingNext Generation ComputingA Roadmap for Agent Based Computing Michael Luck, University of Southampton, UK mml@ecs.soton.ac.uk

4. 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

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

6. 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

7. 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

8. 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

9. 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

10. 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

11. AgentLink and the Roadmap

12. 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)

13. 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

14. 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

15. 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

16. 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

17. 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

18. State of the art

19. 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

20. Agents as Design • Agent oriented software engineering • Agent architectures • Mobile agents • Agent infrastructure • Electronic institutions

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

22. Links to other disciplines • Philosophy • Logic • Economics • Social sciences • Biology

23. 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

24. The Roadmap Timeline

25. Dimensions • Sharing of knowledge and goals • Design by same or diverse teams • Languages and interaction protocols • Scale of agents, users, complexity • Design methodologies

26. 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

27. 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

28. 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

29. 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.

30. The Roadmap Timeline

31. Technological Challenges

32. 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

33. 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

34. 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

35. Industrial Strength Software

36. 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

37. Agreed Standards

38. 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

39. 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

40. Semantic Infrastructure for Open Communities

41. 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

42. Reasoning in Open Environments • Coalition formation • Dynamic establishment of virtual organisations • Demanded by emerging computational infrastructure such as • Grid • Web Services • eBusiness workflow systems

43. 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

44. Reasoning in Open Environments

45. 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

46. Learning Technologies

47. 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

48. Trust and Reputation

49. Challenges for the Agent Community

50. 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