We describe a framework and an algorithm for solving hybrid influence diagrams with discrete, continuous, and deterministic chance variables, and discrete and continuous decision variables. A continuous chance variable in an influence diagram is said to be deterministic if its conditional distributions have zero variances. The solution algorithm is an extension of Shenoy’s fusion algorithm for ...

We extend the logical model of agency known as the KGP model, to support agents with normative concepts, based on the roles an agent plays and the obligations and prohibitions that result from playing these roles. The proposed framework illustrates how the resulting normative concepts, including the roles, can evolve dynamically during the lifetime of the agent. Furthermore, we illustrate how t...

Interactive dynamic influence diagrams (I-DIDs) offer a transparent and semantically clear representation for the sequential decision-making problem over multiple time steps in the presence of other interacting agents. Solving I-DIDs exactly involves knowing the solutions of possible models of the other agents, which increase exponentially with the number of time steps. We present a method of s...

Abstr act: Many real world models can be characterized as weak, m�aning that there is significant uncertainty in both the data input and inferences. This lack of determinism makes it especially difficult for users of computer decision aids to understand and have confidence in the models. This paper presents a representation for uncertainty and utilities that serves as a framework for graphical ...

Modeling the perceived behaviors of other agents improves the performance of an agent in multiagent interactions. We utilize the language of interactive influence diagrams to model repeated interactions between the agents, and ascribe procedural models to other agents. Procedural models offer the benefit of understanding how others arrive at their behaviors. As model spaces are often bounded, t...

In this paper we will survey the history of Influence Diagrams from their origin in Decision Theory to modern AI uses. We will compare the various methods that have been used to find an optimal strategy for a given influence diagram. These methods have various advantages under different assumptions and there is a progression to the present of richer, more general solutions. We also look at an a...

A key challenge in information retrieval is the use of contextual evidence within the ad-hoc retrieval. Our contribution is particularly based on the belief that contextual retrieval is a decision making problem. For this reason we propose to apply influence diagrams which are extension of Bayesian networks to such problems, in order to solve the hard problem of user based relevance estimation....