Despite their different perspectives, artificial intelligence (AI) and the disciplines of decision
science have common roots and strive for similar goals. This paper surveys the potential for …

We are entering an era of more intelligent cognitive vision systems. Such systems can
analyse activity in dynamic scenes to compute conceptual descriptions from motion …

An intelligent agent interacting with the real world will encounter individual people, courses,
test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of …

Reasoning about change requires predicting how long a proposition, having become true,
will continue to be so. Lacking perfect knowledge, an agent may be constrained to believe …

This paper introduces a hierarchical Markov model that can learn and infer a user's daily
movements through an urban community. The model uses multiple levels of abstraction in …

Dynamic probabilistic networks are a compact representation of complex stochastic
processes. In this paper we examine how to learn the structure of a DPN from data. We …

We present a method of learning a Bayesian model of a traveler moving through an urban
environment. This technique is novel in that it simultaneously learns a unified model of the …

A new domain-independent knowledge-based inference structure is presented, specific to
the task of abstracting higher-level concepts from time-stamped data. The framework …

Abstract The Partially Observable Markov Decision Process has long been recognized as a
rich framework for real-world planning and control problems, especially in robotics. However …

Probabilistic networks (also known as Bayesian belief networks) allow a compact
description of complex stochastic relationships among several random variables. They are …