Curious Negotiator

n negotiation the exchange of information is as important as the exchange of offers. The curious negotiator is a multiagent system with three types of agents. Two negotiation agents, each representing an individual, develop consecutive offers, supported by information, whilst requesting information from its opponent. A mediator agent, with experience of prior negotiations, suggests how the negotiation may develop. A failed negotiation is a missed opportunity. An observer agent analyses failures looking for new opportunities. The integration of negotiation theory and data mining enables the curious negotiator to discover and exploit negotiation opportunities. Trials will be conducted in electronic business

Learning Novel Domains Through Curiosity and Conjecture

This paper describes DIDO, a system we have developed to carry out exploratory learning of unfamiliar domains without assistance from an external teacher. The program incorporates novel approaches to experience generation and representation generation. The experience generator uses a heuristic based on Shannon's uncertainty function to find informative examples. The representation generator makes conjectures on the basis of small amounts of evidence and retracts them if they prove to be wrong or useless. A number of experiments are described which demonstrate that the system can distribute its learning resources to steadily acquire a good representation of the whole of a domain, and that the system can readily acquire both disjunctive and conjunctive concepts even in the presence of noise.

Learning Novel Domains Through Curiosity and Conjecture

This paper describes DIDO, a system we have developed to carry out exploratory learning of unfamiliar domains without assistance from an external teacher. The program incorporates novel approaches to experience generation and representation generation. The experience generator uses a heuristic based on Shannon's uncertainty function to find informative examples. The representation generator makes conjectures on the basis of small amounts of evidence and retracts them if they prove to be wrong or useless. A number of experiments arc described which demonstrate that the system can distribute its learning resources to steadily acquire a good representation of the whole of a domain, and that the system can readily acquire both disjunctive and conjunctive concepts even in the presence of noise. 1