Evaluation of Bayesian networks with flexible state-space abstraction methods

We investigate state-space abstraction methods for computing approximate probabilities with Bayesian networks. These methods approximate Bayesian networks by aggregating the states of variables. We implement an iterative approximation procedure based on this idea, and the procedure demonstrates the desirable anytime property in experiments. Further theoretical analysis reveals special properties of the approximations, and we exploit these properties to design heuristics for improving performance profiles of the iterative procedure.

Automated Negotiation from Declarative Contract Descriptions

Our approach for automating the negotiation of business contracts proceeds in three broad steps. First, determine the structure of the negotiation process by applying general knowledge about auctions and domain-specific knowledge about the contract subject along with preferences from potential buyers and sellers. Second, translate the determined negotiation structure into an operational specification for an auction platform. Third, after the negotiation has completed, map the negotiation results to a final contract.We have implemented a prototype which supports these steps by employing a declarative specification (in Courteous Logic Programs) of (1) high-level knowledge about alternative negotiation structures, (2) general-case rules about auction parameters, (3) rules to map the auction parameters to a specific auction platform, and (4) special-case rules for subject domains. We demonstrate the flexibility of this approach by automatically generating several alternative negotiation structures for the domain of travel shopping in a trading agent competition.