Collaborating Authors

A Belief-Function Based Decision Support System Artificial Intelligence

In this paper, we present a decision support system based on belief functions and the pignistic transformation. The system is an integration of an evidential system for belief function propagation and a valuation-based system for Bayesian decision analysis. The two subsystems are connected through the pignistic transformation. The system takes as inputs the user's "gut feelings" about a situation and suggests what, if any, are to be tested and in what order, and it does so with a user friendly interface.

Equivalence Relations in Fully and Partially Observable Markov Decision Processes

AAAI Conferences

Dean & Givan [1997], Ferns et al. [2004], Taylor et We explore equivalence relations between states in al. [2009]). Comparatively little work has focused on bisimulation Markov Decision Processes and Partially Observable for POMDPs, except for a basic definition of a bisimulation Markov Decision Processes. We focus on two notion for POMDP states [Pineau, 2004] (though the different equivalence notions: bisimulation [Givan terminology of "bisimulation" is not used there). To our et al., 2003] and a notion of trace equivalence, under knowledge, trace equivalence has not really been explored in which states are considered equivalent if they either MDPs or POMDPs. However, using traces holds the generate the same conditional probability distributions potential of offering a more efficient and natural way of computing over observation sequences (where the conditioning and approximating state equivalence through sampling is on action sequences). We show that the methods (rather than the global, model-based process used relationship between these two equivalence notions typically to compute bisimulation). Moreover, in POMDPs, changes depending on the amount and nature of the trace equivalence is intimately related to predictive state representations partial observability.

Sampling and Updating Higher Order Beliefs in Decision-Theoretic Bargaining Under Uncertainty

AAAI Conferences

In this paper we study the sequential strategic interactive setting of two-person, two-stage, seller-offers bargaining under uncertainty. We model the epistemology of the problem in a finite interactive decision-theoretic framework and solve it for three types of agents of successively increasing (epistemological) sophistication (or, capacity to represent and reason with higher orders of beliefs). In particular, we remove common knowledge assumptions about the agents' epistemology which, if made, would be sufficient to imply the existence of a, possibly unique, game-theoretic equilibrium solution. In this context, we present a characterization of a monotonic relationship between an agent's optimal behavior and its beliefs under a particular moment-based ordering. Further, based on this characterization, we present the \emph{spread-accumulate} sampling technique -- a method of sampling an agent's higher order belief by generating ``evenly dispersed" beliefs for which we (pre)compute offline solutions. Then, we present a method for approximating higher order prior belief update to arbitrary precision by identifying a (previously solved) belief ``closest" to the true belief. In addition, these methods directly suggest a mechanism for achieving a balance between efficiency and the quality of the approximation -- either by generating a large number of offline solutions or by allowing the agent to search online for a ``closer" belief in the vicinity of best current solution.

Modeling Situation Awareness in Human-Like Agents Using Mental Models

AAAI Conferences

In order for agents to be able to act intelligently in an environment, a first necessary step is to become aware of the current situation in the environment. Forming such awareness is not a trivial matter. Appropriate observations should be selected by the agent, and the observation results should be interpreted and combined into one coherent picture. Humans use dedicated mental models which represent the relationships between various observations and the formation of beliefs about the environment, which then again direct the further observations to be performed. In this paper, a generic agent model for situation awareness is proposed that is able to take a mental model as input, and utilize this model to create a picture of the current situation. In order to show the suitability of the approach, it has been applied within the domain of F-16 fighter pilot training for which a dedicated mental model has been specified, and simulations experiments have been conducted.