A wide range of human reasoning patterns can be explained as conditioning in probabilistic models; however, conditioning has traditionally been viewed as an operation applied to such models, not represented in such models. We describe how probabilistic programs can explicitly represent conditioning as part of a model. This enables us to describe reasoning about others’ reasoning using nested conditioning. Much of human reasoning is about the beliefs, desires, and intentions of other people; we use probabilistic programs to formalize these inferences in a way that captures the flexibility and inherent uncertainty of reasoning about other agents. We express examples from game theory, artificial intelligence, and linguistics as recursive probabilistic programs and illustrate how this representation language makes it easy to explore new directions in each of these fields. We discuss the algorithmic challenges posed by these kinds of models and describe how Dynamic Programming techniques can help address these challenges.

Functional models have been extensively investigated in the context of several problemsolving tasks such as device diagnosis and design. In this paper, we view problem solvers themselves as devices, and use structure-behavior-function models to represent how they work. The model representing the functioning of a problem solver explicitly specifies how the knowledge and reasoning of the problem solver result in the achievement of its goals. Then, we employ these models for performance-driven reflective learning. We view performance-driven learning as the task of redesigning the knowledge and reasoning of the problem solver to improve its performance. We use the model of the problem solver to monitor its reasoning, assign blame when it fails, and appropriately redesign its knowledge and reasoning. This paper focuses on the model-based redesign of a path planner's task structure. It illustrates the modelbased reflection using examples from an operational system called the Autognostic system.

How can we insure that knowledge embedded in a program is applied effectively? Traditionally the answer to this question has been sought in different problem solving paradigms and in different approaches to encoding and indexing knowledge. Each of these is useful with a certain variety of problem, but they all share a common problem: they become ineffective in the face of a sufficiently large knowledge base. How then can we make it possible for a system to continue to function in the face of a very large number of plausibly useful chunks of knowledge? In response to this question we propose a framework for viewing issues of knowledge indexing and retrieval, a framework that includes what appears to be a useful perspective on the concept of a strategy. We view strategies as a means of controlling invocation in situations where traditional selection mechanisms become ineffective. We examine ways to effect such control, and describe meta-rules, a means of specifying strategies which offers a number of advantages. We consider at some length how and when it is useful to reason about control, and explore the advantages meta-rules offer for doing this.

This is an informal description of my ideas about using formal logic as a tool for reasoning systems using computers. The theoretical ideas are illustrated by the features of FOL. All of the examples presented have actually run using the FOL system.