Negotiating intelligently

The predominant approaches to automating competitive interaction appeal to the central notion of a utility function that represents an agent's preferences. Agent's are then endowed with machinery that enables them to perform actions that are intended to optimise their expected utility. Despite the extent of this work, the deployment of automatic negotiating agents in real world scenarios is rare. We propose that utility functions, or preference orderings, are often not known with certainty; further, the uncertainty that underpins them is typically in a state of flux. We propose that the key to building intelligent negotiating agents is to take an agent's historic observations as primitive, to model that agent's changing uncertainty in that information, and to use that model as the foundation for the agent's reasoning. We describe an agent architecture, with an attendant theory, that is based on that model. In this approach, the utility of contracts, and the trust and reliability of a trading partner are intermediate concepts that an agent may estimate from its information model. This enables us to describe intelligent agents that are not necessarily utility optimisers, that value information as a commodity, and that build relationships with other agents through the trusted exchange of information as well as contracts.