Abstract:
Approaches to the construction of agents that are to engage in competitive negotiation are often founded
on game theory. In such an approach the agents are endowed with utility functions and assumed to be utility
optimisers. In practice the utility function is derived in the context of massive uncertainties both in terms of the
agent's priorities and of the raw data or information. To address this issue we propose an agent architecture that
is founded on information theory, and that manages uncertainty with entropy-based inference. Our negotiating
agent engages in multi-issue bilateral negotiation in a dynamic information-rich environment. The agent
strives to make informed decisions. The agent may assume that the integrity of some of its information decays
with time, and that a negotiation may break down under certain conditions. The agent makes no assumptions
about the internals of its opponent - it focuses only on the signals that it receives. It constructs two probability
distributions over the set of all deals. First the probability that its opponent will accept a deal, and second that
a deal will prove to be acceptable to it in time.
