Agents, Infrastructure, Applications and Norms

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Agents, Infrastructure, Applications and Norms

• Michael Luck
• University of Southampton, UK

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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

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Agent Technology EnablingNext Generation
ComputingA Roadmap for Agent Based Computing

• Michael Luck, University of Southampton, UK
• mml_at_ecs.soton.ac.uk

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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

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What is an agent?

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

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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

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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

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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

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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

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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

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AgentLink and the Roadmap
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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)

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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 RD

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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

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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 RD efforts

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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

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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

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State of the art
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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

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Agents as Design

• Agent oriented software engineering
• Agent architectures
• Mobile agents
• Agent infrastructure
• Electronic institutions

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Agent technologies

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

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Links to other disciplines

• Philosophy
• Logic
• Economics
• Social sciences
• Biology

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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

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The Roadmap Timeline
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Dimensions

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

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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

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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

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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

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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.

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The Roadmap Timeline
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Technological Challenges
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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

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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

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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

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Industrial Strength Software
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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

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Agreed Standards
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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

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Semantic Infrastructure for Open Communities

• Ontologies
• DAMLOIL
• UML
• OWL
• Timely covergence of technologies
• Generic tool and service support
• Shared ontologies
• Semantic Web community exploring many questions

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Semantic Infrastructure for Open Communities
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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

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Reasoning in Open Environments

• Coalition formation
• Dynamic establishment of virtual organisations
• Demanded by emerging computational infrastructure
  such as
• Grid
• Web Services
• eBusiness workflow systems

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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

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Reasoning in Open Environments
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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

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Learning Technologies
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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

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Trust and Reputation
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Challenges for the Agent Community
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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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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

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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.

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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.

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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.

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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.

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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 todays solutions,
  

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Application Opportunities
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Application Opportunities

• Ambient Intelligence
• Bioinformatics and Computational Biology
• Grid Computing
• Electronic Business
• Simulation
• Semantic Web

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Ambient Intelligence

• Pillar of European Commissions 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

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Ambient Intelligence

• Suggests a component-oriented world populated by
  agents
• Autonomy
• Distribution
• Adaptation
• Responsiveness
• Demands
• Virtual organisations
• Infrastructure
• Scalability

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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

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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

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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

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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

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Simulation

• Education and training
• Scenario exploration
• Entertainment

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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).

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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

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For more information ...

• Dr Michael Luck
• Department of Electronics and
• Computer Science
• University of Southampton
• Southampton SO17 1BJ
• United Kingdom
• Feedback sought please send feedback!
• Roadmap www.agentlink.org/roadmap

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The Book
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The CD
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The Agent Portalwww.agentlink.org