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Agents, Infrastructure, Applications and Norms

Agents, Infrastructure, Applications and Norms. Michael Luck University 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

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Agents, Infrastructure, Applications and Norms

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  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

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