Belief Manipulation Through Propositional Announcements

Public announcements cause each agent in a group to modify their beliefs to incorporate some new piece of information, while simultaneously being aware that all other agents are doing the same. Given a set of agents and a set of epistemic goals, it is natural to ask if there is a single announcement that will make each agent believe the corresponding goal. This problem is known to be undecidable in a general modal setting, where the presence of nested beliefs can lead to complex dynamics. In this paper, we consider not necessarily truthful public announcements in the setting of AGM belief revision. We prove that announcement finding in this setting is not only decidable, but that it is simpler than the corresponding problem in the most simplified modal logics. We then describe AnnB, an implemented tool that uses announcement finding as the basis for controlling robot behaviour through belief manipulation.

The Minds of Many: Opponent Modeling in a Stochastic Game

The Theory of Mind provides a framework for an agent to predict the actions of adversaries by building an abstract model of their strategies using recursive nested beliefs. In this paper, we extend a recently introduced technique for opponent modeling based on Theory of Mind reasoning. Our extended multi-agent Theory of Mind model explicitly considers multiple opponents simultaneously. We introduce a stereotyping mechanism, which segments the agent population into sub-groups of agents with similar behavior. Here, sub-group profiles guide decision making in place of individual agent profiles. We evaluate our model using a multi-player stochastic game, which presents agents with the challenge of unknown adversaries in a partially-observable environment. Simulation results demonstrate that the model performs well under uncertainty and that stereotyping allows larger groups of agents to be modeled robustly. The findings strengthen results showing that Theory of Mind modeling is useful in many artificial intelligence applications.

SOME COMPLETENESS THEOREMS IN THE DYNAMIC DOXASTIC LOGIC OF ITERATED BELIEF REVISION

The success of the AGM paradigm—the theory of belief change initiated by Alchourrón, Gärdenfors, and Makinson—is remarkable, as even a quick look at the literature it has generated will testify. But it is also remarkable, at least in hindsight, how limited was the original effort. For example, the theory concerns the beliefs of just one agent; all incoming information is accepted; belief change is uniquely determined by the new information; there is no provision for nested beliefs. And perhaps most surprising: there is no analysis of iterated change.In this paper it is that last restriction that is at issue. Our medium of study is dynamic doxastic logic (DDL). The success of the AGM paradigm The particular contribution of the paper is detailed completeness proofs for three dynamic doxastic logics of iterated belief revision.The problem of extending the AGM paradigm to include iterated change has been discussed for years, but systematic discussions have appeared only recently (see Segerberg, 2007 and forthcoming, but also van Benthem, 2007; Rott, 2006; Zvesper, 2007).