Tech Reports
Tech Report kmi-04-10 Abstract
Modelling Agents Behaviour in Automated Negotiation
Techreport ID: kmi-04-10
Date: 2004
Author(s): Chongming Hou
This paper presents a learning mechanism that applies nonlinear regression analysis to model a negotiation agents behaviour based only on the opponent's previous offers. The behaviour of negotiation agents in this study is determined by their tactics in the form of decision functions. Heuristics based on estimates of an agents tactics are drawn from a series of experiments. By applying the nonlinear regression and the obtained heuristic knowledge, an agent can improve their overall performance by predicting the opponents deadline and reservation value, terminating pointless negotiation, and avoiding negotiation breakdown. The findings of this study show that this approach can be used to obtain better deals than previously proposed tactics. The learning mechanism can be used online, without any prior knowledge about the other agents and is therefore, very useful in open systems where agents have little or no information about each other.
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We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.
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