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

A heuristic information retrieval study : an investigation of methods for enhanced searching of distributed data objects exploiting bidirectional relevance feedback

A thesis submitted for the degree of Doctor of Philosophy of the University of LutonThe primary aim of this research is to investigate methods of improving the effectiveness of current information retrieval systems. This aim can be achieved by accomplishing numerous supporting objectives. A foundational objective is to introduce a novel bidirectional, symmetrical fuzzy logic theory which may prove valuable to information retrieval, including internet searches of distributed data objects. A further objective is to design, implement and apply the novel theory to an experimental information retrieval system called ANACALYPSE, which automatically computes the relevance of a large number of unseen documents from expert relevance feedback on a small number of documents read. A further objective is to define a methodology used in this work as an experimental information retrieval framework consisting of multiple tables including various formulae which anow a plethora of syntheses of similarity functions, ternl weights, relative term frequencies, document weights, bidirectional relevance feedback and history adjusted term weights. The evaluation of bidirectional relevance feedback reveals a better correspondence between system ranking of documents and users' preferences than feedback free system ranking. The assessment of similarity functions reveals that the Cosine and Jaccard functions perform significantly better than the DotProduct and Overlap functions. The evaluation of history tracking of the documents visited from a root page reveals better system ranking of documents than tracking free information retrieval. The assessment of stemming reveals that system information retrieval performance remains unaffected, while stop word removal does not appear to be beneficial and can sometimes be harmful. The overall evaluation of the experimental information retrieval system in comparison to a leading edge commercial information retrieval system and also in comparison to the expert's golden standard of judged relevance according to established statistical correlation methods reveal enhanced system information retrieval effectiveness

Multi-agent opportunism

The real world is a complex place, rife with uncertainty; and prone to rapid change. Agents operating in a real-world domain need to be capable of dealing with the unexpected events that will occur as they carry out their tasks. While unexpected events are often related to failures in an agent\u27s plan, or inaccurate knowledge in an agent\u27s memory, they can also be opportunities for the agent. For example, an unexpected event may present the opportunity to achieve a goal that was previously unattainable. Similarly, real-world multi-agent systems (MASs) can benefit from the ability to exploit opportunities. These benefits include the ability for the MAS itself to better adapt to its changing environment, the ability to ensure agents obtain critical information in a timely fashion, and improvements in the overall performance of the system. In this dissertation we present a framework for multi-agent opportunism that is applicable to open systems of heterogeneous planning agents. The contributions of our research are both theoretical and practical. On the theoretical side, we provide an analysis of the critical issues that must be addressed in order to successfully exploit opportunities in a multi-agent system. This analysis can provide MAS designers and developers important guidance to incorporate multi-agent opportunism into their own systems. It also provides the fundamental underpinnings of our own specific approach to multi-agent opportunism. On the practical side, we have developed, implemented, and evaluated a specific approach to multi-agent opportunism for a particular class of multi-agent system. Our evaluation demonstrates that multi-agent opportunism can indeed be effective in systems of heterogeneous agents even when the amount of knowledge the agents share is severely limited. Our evaluation also demonstrates that agents that are capable of exploiting opportunities for their own goals are also able, using the same mechanisms, to recognize and respond to potential opportunities for the goals of other agents. Further and perhaps more interesting, we show that under some circumstances, multi-agent opportunism can be effective even when the agents are not themselves capable of single-agent opportunism

Extending Multi-Agent Cooperation by Overhearing

Much cooperation among humans happens following a common pattern: by chance or deliberately, a person overhears a conversation between two or more parties and steps in to help, for instance by suggesting answers to questions, by volunteering to perform actions, by making observations or adding information. We describe an abstract architecture to support a similar pattern in societies of artificial agents. Our architecture involves pairs of so-called service agents (or services) engaged in some tasks, and unlimited number of suggestive agents (or suggesters). The latte