Representing Conversations for Scalable Overhearing

Open distributed multi-agent systems are gaining interest in the academic community and in industry. In such open settings, agents are often coordinated using standardized agent conversation protocols. The representation of such protocols (for analysis, validation, monitoring, etc) is an important aspect of multi-agent applications. Recently, Petri nets have been shown to be an interesting approach to such representation, and radically different approaches using Petri nets have been proposed. However, their relative strengths and weaknesses have not been examined. Moreover, their scalability and suitability for different tasks have not been addressed. This paper addresses both these challenges. First, we analyze existing Petri net representations in terms of their scalability and appropriateness for overhearing, an important task in monitoring open multi-agent systems. Then, building on the insights gained, we introduce a novel representation using Colored Petri nets that explicitly represent legal joint conversation states and messages. This representation approach offers significant improvements in scalability and is particularly suitable for overhearing. Furthermore, we show that this new representation offers a comprehensive coverage of all conversation features of FIPA conversation standards. We also present a procedure for transforming AUML conversation protocol diagrams (a standard human-readable representation), to our Colored Petri net representation

Ensuring consistency in the joint beliefs of interacting agents

Agent interaction in realistic applications is subject to many forms of uncertainty - including information and network uncertainty, trust of and conflicts with other participants, lack of stability in a deal and risks about agreements and commitments. However, one of the most common forms of uncertainty occurs when a group has divergent beliefs about the interaction they are engaged in -- some agents believe an agreement has been reached, while others believe it has been rejected or that they are still bargaining. Such misunderstandings can arise because of loss of network performance, spurious connections, message loss or delays. Against this background, this paper develops synchronisation protocols for a group of agents to attain the same beliefs about an interaction, independent of the reliability of the underlying communication layer. This paper includes and proves theorems about a group's mutual beliefs, on which the safety of an interaction relies. Specifically, protocols for message exchange and belief revision and the reasoning for reachability of states during interactions are presented. Each protocol is proved to show that an increasing level of mutual and consistent belief is reached, thereby guaranteeing an interaction's integrity

Reliable group communication and institutional actions in a multi-agent trading scenario

The use of asynchronous communication is traditionally seen to be an important element of an agent’s autonomy. This paper argues that groups of agents within a society need the ability to choose forms of communication with stronger guarantees for particular interactions, and in particular, focuses on the use of reliable group communication. An example electronic trading scenario — the game of Pit — is presented, and it is shown how a formal institution for a particular critical phase of Pit can be built on top of the semantics for totally ordered and virtually synchronous multicasting.UnpublishedK. P. Birman and T. A. Joseph. Reliable communication in the presence of failures. ACM Transactions on Computer Systems, 5(1):47–76, 1987. P. Busetta, A. Donà, and M. Nori. Channeled multicast for group communications. In Proceedings of the 1st International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2002), pages 1280–1287. ACM Press, 2002. D. R. Cheriton and D. Skeen. Understanding the limitations of causally and totally ordered communication. Operating Systems Review, 27(5):44–57, 1993. (Proceedings of the Fourteenth ACM Symposium on Operating System Principles). M. Colombetti. Agent communication. Unpublished seminar notes, University of Otago, 2003. U. Cortés. Electronic institutions and agents. AgentLink News, 15:14–15, September 2004. http://www.agentlink.org/newsletter/15/AL-15.pdf. D. Dolev and D. Malki. The Transis approach to high availability cluster computing. Communications of the ACM, 39(4):64–70, 1996. E. Emerson and J. Halpern. “Sometimes” and “not never” revisited: On branching versus linear time temporal logic. Journal of the ACM, 33(1):151–178, 1986. R. Fagin, J.Y.Halpern, Y. Moses, and M. Y. Vardi. Reasoning about Knowledge. MIT Press, Cambridge, MA, 1995. T. Finin, Y. Labrou, and J. Mayfield. KQML as an agent communication language. In J. M. Bradshaw, editor, Software Agents. MIT Press, 1997. FIPA. FIPA ACL message representation in string specification, Foundation for Intelligent Physical Agents. http://www.fipa.org/specs/fipa00070/, 2002. JGroups project home page. http://www.jgroups.org, 2004. S. Kumar, M. J. Huber, D. McGee, P. R. Cohen, and H. J. Levesque. Semantics of agent communication languages for group interaction. In Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI 2000), pages 42–47. AAAI Press / MIT Press, 2000. A. U. Mallya, P. Yolum, and M. P. Singh. Resolving commitments among autonomous agents. In F. Dignum, editor, Advances in Agent Communication, International Workshop on Agent Communication Languages, ACL 2003, volume 2922 of Lecture Notes in Computer Science, pages 166–182. Springer, 2004. G. Neiger and S. Toueg. Simulating synchronized clocks and common knowledge in distributed systems. Journal of the ACM, 40(2):334–367, 1993. Object Management Group. UML 2.0 superstructure final adopted specification. Document ptc/03-08-02, http://www.omg.org/cgi-bin/doc?ptc/2003-08-02, 2003. Parker Brothers. Pit rules. http://www.hasbro.com/common/instruct/pit.pdf, 1904. S. Paurobally, J. Cunningham, and N. R. Jennings. Ensuring consistency in the joint beliefs of interacting agents. In Proceedings of the 2nd International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2003), pages 662–669. ACM Press, 2003. M. Purvis, M. Nowostawski, S. Cranefield, and M. Oliveira. Multi-agent interaction technology for peer-to-peer computing in electronic trading environments. In C. Zhang, H. W. Guesgen, and W. Yeap, editors, Proceedings of the 8th Pacific Rim International Conference on Artificial Intelligence, volume 3157 of Lecture Notes In Artificial Intelligence, pages 625–634. Springer, 2004. H. van Ditmarsch. Some game theory of Pit. In C. Zhang, H. W. Guesgen, and W. Yeap, editors, Proceedings of the 8th Pacific Rim International Conference on Artificial Intelligence, volume 3157 of Lecture Notes in Artificial Intelligence, pages 946–947. Springer, 2004. M. Verdicchio and M. Colombetti. A logical model of social commitment for agent communication. In Proceedings of the 2nd International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2003), pages 528–535. ACM Press, 2003. M. Verdicchio and M. Colombetti. A logical model of social commitment for agent communication. In F. Dignum, editor, Advances in Agent Communication, International Workshop on Agent Communication Languages, ACL 2003, volume 2922 of Lecture Notes in Computer Science, pages 128–145. Springer, 2004. M. Wooldridge and N. R. Jennings. Intelligent agents: Theory and practice. Knowledge Engineering Review, 10(2):115–152, 1995

Reliable group communication and institutional actions in a multi-agent trading scenario

The use of asynchronous communication is traditionally seen to be an important element of an agent’s autonomy. This paper argues that groups of agents within a society need the ability to choose forms of communication with stronger guarantees for particular interactions, and in particular, focuses on the use of reliable group communication. An example electronic trading scenario — the game of Pit — is presented, and it is shown how a formal institution for a particular critical phase of Pit can be built on top of the semantics for totally ordered and virtually synchronous multicasting.UnpublishedK. P. Birman and T. A. Joseph. Reliable communication in the presence of failures. ACM Transactions on Computer Systems, 5(1):47–76, 1987. P. Busetta, A. Donà, and M. Nori. Channeled multicast for group communications. In Proceedings of the 1st International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2002), pages 1280–1287. ACM Press, 2002. D. R. Cheriton and D. Skeen. Understanding the limitations of causally and totally ordered communication. Operating Systems Review, 27(5):44–57, 1993. (Proceedings of the Fourteenth ACM Symposium on Operating System Principles). M. Colombetti. Agent communication. Unpublished seminar notes, University of Otago, 2003. U. Cortés. Electronic institutions and agents. AgentLink News, 15:14–15, September 2004. http://www.agentlink.org/newsletter/15/AL-15.pdf. D. Dolev and D. Malki. The Transis approach to high availability cluster computing. Communications of the ACM, 39(4):64–70, 1996. E. Emerson and J. Halpern. “Sometimes” and “not never” revisited: On branching versus linear time temporal logic. Journal of the ACM, 33(1):151–178, 1986. R. Fagin, J.Y.Halpern, Y. Moses, and M. Y. Vardi. Reasoning about Knowledge. MIT Press, Cambridge, MA, 1995. T. Finin, Y. Labrou, and J. Mayfield. KQML as an agent communication language. In J. M. Bradshaw, editor, Software Agents. MIT Press, 1997. FIPA. FIPA ACL message representation in string specification, Foundation for Intelligent Physical Agents. http://www.fipa.org/specs/fipa00070/, 2002. JGroups project home page. http://www.jgroups.org, 2004. S. Kumar, M. J. Huber, D. McGee, P. R. Cohen, and H. J. Levesque. Semantics of agent communication languages for group interaction. In Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI 2000), pages 42–47. AAAI Press / MIT Press, 2000. A. U. Mallya, P. Yolum, and M. P. Singh. Resolving commitments among autonomous agents. In F. Dignum, editor, Advances in Agent Communication, International Workshop on Agent Communication Languages, ACL 2003, volume 2922 of Lecture Notes in Computer Science, pages 166–182. Springer, 2004. G. Neiger and S. Toueg. Simulating synchronized clocks and common knowledge in distributed systems. Journal of the ACM, 40(2):334–367, 1993. Object Management Group. UML 2.0 superstructure final adopted specification. Document ptc/03-08-02, http://www.omg.org/cgi-bin/doc?ptc/2003-08-02, 2003. Parker Brothers. Pit rules. http://www.hasbro.com/common/instruct/pit.pdf, 1904. S. Paurobally, J. Cunningham, and N. R. Jennings. Ensuring consistency in the joint beliefs of interacting agents. In Proceedings of the 2nd International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2003), pages 662–669. ACM Press, 2003. M. Purvis, M. Nowostawski, S. Cranefield, and M. Oliveira. Multi-agent interaction technology for peer-to-peer computing in electronic trading environments. In C. Zhang, H. W. Guesgen, and W. Yeap, editors, Proceedings of the 8th Pacific Rim International Conference on Artificial Intelligence, volume 3157 of Lecture Notes In Artificial Intelligence, pages 625–634. Springer, 2004. H. van Ditmarsch. Some game theory of Pit. In C. Zhang, H. W. Guesgen, and W. Yeap, editors, Proceedings of the 8th Pacific Rim International Conference on Artificial Intelligence, volume 3157 of Lecture Notes in Artificial Intelligence, pages 946–947. Springer, 2004. M. Verdicchio and M. Colombetti. A logical model of social commitment for agent communication. In Proceedings of the 2nd International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2003), pages 528–535. ACM Press, 2003. M. Verdicchio and M. Colombetti. A logical model of social commitment for agent communication. In F. Dignum, editor, Advances in Agent Communication, International Workshop on Agent Communication Languages, ACL 2003, volume 2922 of Lecture Notes in Computer Science, pages 128–145. Springer, 2004. M. Wooldridge and N. R. Jennings. Intelligent agents: Theory and practice. Knowledge Engineering Review, 10(2):115–152, 1995