A Decision Theoretic Approach for Interface Agent Development

The complexity of current software applications is overwhelming users. The need exists for intelligent interface agents to address the problem of increasing taskload that is overwhelming the human user. Interface agents could help alleviate user taskload by extracting and analyzing relevant information, and providing information abstractions of that information, and providing timely, beneficial assistance to users. These agents could communicate with the user through the existing user interface and also adapt to user needs and behaviors. Central to providing assistance to a user is the issue of correctly determining the user\u27s intent. This dissertation presents an effective, efficient, and extensible decision-theoretic architecture for user intent ascription. The multi-agent architecture, the Core Interface Agent architecture, provides a dynamic, uncertainty-based knowledge representation for modeling the inherent ambiguity in ascribing user intent. The knowledge representation, a Bayesian network, provides an intuitive, mathematically sound way of determining the likelihood a user is pursuing a goal. This likelihood, combined with the utility of offering assistance to the user, provides a decision-theoretic approach to offering assistance to the user. The architecture maintains an accurate user model of the user\u27s goals within a target system environment. The on-line maintenance of the user model is performed by a collection of correction adaptation agents. Because the decision-theoretic methodology is domain-independent, this new methodology for user intent ascription is readily extensible over new application domains. Furthermore, it also offers the ability to bootstrap intent understanding without the need for often lengthy and costly knowledge elicitation. Thus, as a side benefit, the process can mitigate the classic knowledge acquisition bottleneck problem

CAPIR: Collaborative Action Planning with Intention Recognition

We apply decision theoretic techniques to construct non-player characters that are able to assist a human player in collaborative games. The method is based on solving Markov decision processes, which can be difficult when the game state is described by many variables. To scale to more complex games, the method allows decomposition of a game task into subtasks, each of which can be modelled by a Markov decision process. Intention recognition is used to infer the subtask that the human is currently performing, allowing the helper to assist the human in performing the correct task. Experiments show that the method can be effective, giving near-human level performance in helping a human in a collaborative game.Comment: 6 pages, accepted for presentation at AIIDE'1

Framing the Game: Objections to Bapat’s Game-Theoretic Modeling of the Afghan Surge

In a recently published article in the prestigious journal Foreign Policy Analysis, Navin A. Bapat uses a rationalist approach to explain key bargaining processes related to the Afghanistan conflict, concluding that “the Afghan mission may continue for political reasons until it is impossible to sustain militarily.” The article captures the essence of the strategic situation in Afghanistan: the losing dynamic involved. This brief commentary in response is an attempt to shed light on where the tenets of Bapat’s game-theoretic model may be erroneous, even while the model does produce conclusions that appear valid overall

An Empirical Comparison of Three Inference Methods

In this paper, an empirical evaluation of three inference methods for uncertain reasoning is presented in the context of Pathfinder, a large expert system for the diagnosis of lymph-node pathology. The inference procedures evaluated are (1) Bayes' theorem, assuming evidence is conditionally independent given each hypothesis; (2) odds-likelihood updating, assuming evidence is conditionally independent given each hypothesis and given the negation of each hypothesis; and (3) a inference method related to the Dempster-Shafer theory of belief. Both expert-rating and decision-theoretic metrics are used to compare the diagnostic accuracy of the inference methods.Comment: Appears in Proceedings of the Fourth Conference on Uncertainty in Artificial Intelligence (UAI1988