Abstracting Communication in Distributed Agent-Based Systems

1. Abstracting Communication in Distributed Agent-Based Systems Monique Calisti ECOOP 02 10 June 2002 Whitestein Technologies AG Zürich, Switzerland

2. Agenda • Motivation • The Software Agent-Oriented Approach • Communication in Multi-Agent Systems • Experimental feedback • Discussion items • Conclusion

3. Motivation • Communication between distributed software entities populating electronic systems is a crucial aspect for many applications • Even more critical for agent-based systems since any solution relies upon the concept of social behaviour or interaction • Describe the asbtract components for agent communication • Discuss practical experience to identify benefit, limits and potential re-usability

4. What is an agent? • “An over-used term” (Patti Maes, MIT Labs, 1996) • “Agent” can be considered as a theoretical concept from A.I. • A computational system which: • Is long-lived; • Has goals, sensors and effectors; • Decides autonomously which action to take in the current situation to maximize progress toward its (time varying) goals. • Many different definitions exist in the literature • We are going to define a concrete model of what an agent is by considering what abilities and capabilities we expect from agents.

5. “ Our„ answer • An agent is an entity which is: • Situatedin some environment. • Autonomous, in the sense that the system can act without direct intervention from others (humans or other software processes), and that should have control over its own actions and internal state. • Flexiblewhich means: • Responsive: agents should perceive their environment and respond to changes that occur in it in a timely fashion • Pro-active: agents should not simply act in response to their environment, they should be able to exhibit opportunistic, goal-directed behavior and take the initiative when appropriate • Social: agents should be able to interact , when appropriate, with humans or other artificial agents “A Roadmap of agent research and development”,N. Jennings, K. Sycara, M. Wooldridge

6. Communication in MAS • A multi-agent system (MAS) is a system containing more than one agent in which agents can interact and hence influence each other‘s behaviour. • Groups of agents can do things that individuals cannot • Routing over distributed domains, meeting schedule, etc. • Diversity can introduce heterogeneity • Different agents can be specialized in different tasks • Autonomy and self-interest can lead to global unacceptable situations (solutions) • Buyer-seller scenario WHY COMMUNICATION?

7. Agent Communication Stack • A multi-layered infrastructure that enables the decoupling of low-level data transport from high level semantic interoperability aspects

8. Conversational Level • Ongoing conversations between agents often fall into typical patterns. In such cases, certain message sequences are expected, and, at any point in the conversation, other messages are expected to follow. These typical patterns of message exchange are called interaction protocols. • Standard FIPA interaction protocols: • fipa-request • fipa-query • fipa-contract-net request not-understood refuse(reason) agree I request you I agree to... failure(reason) inform (done) inform-ref

9. Agent Messages • The Agent Communication Language (ACL) is an high-level interaction language (propositional attitudes) • Inform, request, cfp, agree, etc. • Ex: KQML, FIPA-ACL • The knowledge interchange format is given by a common content language (propositional) • Action, objects, propositions • Ex: KIF, FIPA-SL, FIPA-CCL, etc. • A common vocabulary and agreed upon definitions to describe a subject domain represent the ontology • cuisine-ontology, cinema-ontology, I inform I request agent B request inform ACL Content Language ontology

10. Example (request :sender (:name moniqueagent@liawww.epfl.ch:8080) :receiver (:name movenpick-hotel@tcp://movenpick.com:6600) :ontology personal-travel-assistant :language FIPA-SL :protocol fipa-request :content (action (book-hotel (:arrival 25/07/2002) (:departure 05/07/2002) ... ) )

11. Experimental Feedback • Multi-provider service provisioning (MuSS) • Electronic financial support system (FAT) • Coexistence of: • Heterogeneous • Self-interested software entities that need to exhibit cooperative behaviour and autonomously interact • FIPA interaction protocols (both) • FIPA-ACL (both) • Content language: • Ad hoc CL (MuSS) • FIPA-SL (FAT) • Ontology: • Implicit ontoloy (MuSS) • Explicit ontology (FAT)

12. Challenges • Strong connection between decision making process and capability to deal with uncertainty: unanticipated conversations • Flexibility of communication components for dynamic adaptation: trade-off • Dynamic modification of deployed communication components • From syntactic to semantic interoperability: sharing the meaning of every deployed component • World heterogeneity: implicit versus explicit components choice

13. Discussion • Layered approach: decoupling low level transport issues from high level interoperability is effective in terms of • Flexible re-use of communication components for different systems • Interoperability of solutions • Better focus on coordination aspects • How effective is the composition of different elements? • Trade-off against the complexity of reasoning systems capable of dealing with every deployed formalism • How much domain-dependent knowledge should go into the deployed communication elements? • Trade-off against the meta-level models/components agents and/or programmers share

14. Conclusion • Common issues to both agent and non-agent based distributed open systems • Flexible coordination is strongly dependent on how communication takes place • The most appropriate level of abstraction should be traded off against the complexity of every single solution • Many efforts in several communities could benefit from a closer cooperation: • DAML, DAML + OIL, Semantic Web, OntoWeb, FIPA, Web services??? • How will the future communication look like?