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Understanding Agents in Service-Oriented Computing: Key Concepts and Applications

This chapter explores the role of agents in service-oriented computing (SOC), defining what an agent is and the functionalities it encompasses. It covers various aspects of agent modeling, including autonomy, interaction, and communication, as well as the complexities involved in agent-based service composition. The chapter highlights the importance of beliefs, desires, and intentions in agent behavior, and introduces frameworks like BDI (Belief-Desire-Intention). It also discusses the integration of semantic web technologies, like OWL-S, to enhance service descriptions and facilitate interoperability.

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Understanding Agents in Service-Oriented Computing: Key Concepts and Applications

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  1. Chapter 15:Agents Service-Oriented Computing: Semantics, Processes, Agents– Munindar P. Singh and Michael N. Huhns, Wiley, 2005

  2. Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Highlights of this Chapter • Agents Introduced • Agent Descriptions • Abstractions for Composition • Describing Compositions • Service Composition as Planning • Rules

  3. Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns What is an Agent? The term agent in computing covers a wide range of behavior and functionality • An agent is an active computational entity • With a persistent identity • Perceives, reasons about, and initiates activities in its environment • Communicates (with other agents) and changes its behavior based on others • Business partners => agents

  4. Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Agents and MAS for SOC • Why agents for services? • Autonomy, heterogeneity, dynamism • Unlike objects, agents • Are proactive and autonomous • Cooperate or compete • Model users, themselves, others • Dynamically use and reconcile ontologies

  5. Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Modeling Agents: AI Traditionally, emphasize mental concepts Beliefs: agent’s representation of the world Knowledge: (usually) true beliefs Desires: preferred states of the world Goals: consistent desires Intentions: goals adopted for action

  6. Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Modeling Agents: MAS • Emphasize interaction • Social: about collections of agents • Organizational: about teams and groups • Legal: about contracts and compliance • Ethical: about right and wrong actions • Emphasize autonomy and communication

  7. Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Mapping SOC to Agents Agents as components of an open system • Autonomy => ability to enter into and enact contracts; compliance • Heterogeneity => ontologies • Loose coupling => communication • Trustworthiness => contracts, ethics, learning, incentives • Dynamism => combination of the above

  8. Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns A Reactive Agent Environment e; RuleSet r; while (true) { state = senseEnvironment(e); a = chooseAction(state, r); e.applyAction(a); }

  9. Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns A Rational Agent Rationality depends on ... • A performance measure, e.g., expected utility • What the agent has perceived so far • What the agent knows ahead of time • The actions the agent can perform An ideal rational agent:for each possible percept sequence, it acts to maximize its expected utility, on the basis of its knowledge and the evidence from the percept sequence

  10. Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Logic-Based Agents • An agent is a knowledge-based system • Explicitly represents symbolic model of the world • Reasons symbolically via logical deduction • Challenges: • Maintaining adequate descriptions of the world • Representing information about complex real-world entities in symbolic terms • Easier in information environments than in general

  11. Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Cognitive Architecture for an Agent For SOC, sensors and effectors are services; communication is via messaging middleware

  12. Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Sensor input brf beliefs Generate options desires filter intentions action Generic BDI Architecture A BDI architecture addresses how beliefs, desires and intentions are represented, updated, and acted upon Action output

  13. Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Architecture of BDI-Based Agent Execution Cycle: the agent • Receives new information • Updates beliefs and goals • Reasons about actions • Intends an action • Selects an intended action • Activates selected intention • Performs an action • Updates beliefs, goals, intentions

  14. Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Web Ontology Language for Services (OWL-S) An OWL-S service description provides • Declarative ads for properties and capabilities, used for discovery • Declarative APIs, used for execution • A declarative description of services • Based on their inputs, outputs, preconditions, and effects • Used for composition and interoperation

  15. Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns OWL-S Service Ontology

  16. Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns OWL-S Compared to UDDI

  17. Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns OWL-S Service Model

  18. Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns OWL-S Example: Processing Book Orders

  19. Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns OWL-S IOPEs for Bookstore Example

  20. Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Composition as Planning • Service composition as planning: • Represent current and goal states • Represent each service as an action (with inputs, outputs, preconditions, effects) • Represent a composed service as a plan that invokes the constituent services constraining the control and data flow to achieve the goal state

  21. Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Rules: Logical Representations • Rules are desirable because they are • Modular: easy to read and maintain • Inspectable: easy to understand • Executable: no further translation needed • Expressive: (commonly) Turing complete and can capture knowledge that would otherwise not be captured declaratively • Compare with relational calculus (classical SQL) or description logics (OWL) • Declarative, although imperfectly so

  22. Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Kinds of Rules • ECA or Reaction • On event if condition then perform action • Derivation rules: special case of above • Integrity constraints: derive false if error • Inference rules • If antecedent then consequent • Support multiple computational strategies • Forward chaining; backward chaining

  23. Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Applying ECA Rules • Capture protocols, policies, and heuristics as rules • Examples? • Often, combine ECA with inference rules (to check if a condition holds) • Modeling challenge • What is an event? • How to capture composite events by pushing event detection to lower layers

  24. Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Applying Inference Rules • Inference rules capture general requirements well • Elaboration tolerance requires defeasibility • Write general rules • Override them as need to specialize them to account for context • Leads to logical nonmonotonicity • Easy enough operationally but difficult to characterize mathematically • Details get into logic programming with negation

  25. Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Use of Variables • Need free variables to make the rules generic in how they apply • For ECA rules: event and condition • For inference rules: antecedent • Should generally not have free variables in consequent to ensure “safety” • Free variable in action indicates perform action for each binding • Free variable in consequent means assert it for each binding

  26. Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Chapter 15 Summary • Agents are natural fit with open environments • Agent abstractions support expressing requirements in a natural manner • Agents go beyond objects and procedural programming • Self-study Jess

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