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…

The Artificial Intelligence Laboratory proposes to continue its work on a group of closely interconnected projects, all bearing on questions about how to make computers able to use more sophisticated kinds of knowledge to solve difficult problems. This proposal explains…

Computers open up new ways to think about knowledge and learning. Learning computer science should draw upon and feed these new approaches. In a previous paper called "Leading a Child to a Computer Culture" I discuss some ways to do…

In a Communication Bootstrapping system, peer components with different perceptual worlds invent symbols and syntax based on correlations between their percepts. I propose that Communication Bootstrapping can also be used to acquire functional definitions of words and causal reasoning knowledge.…

This is a partial theory of thinking, combining a number of classical and modern concepts from psychology, linguistics, and AI. Whenever one encounters a new situation (or makes a substantial change in one's viewpoint) he selects from memory a structure…

This thesis considers three complications that arise from applying reinforcement learning to a real-world application. In the process of using reinforcement learning to build an adaptive electronic market-maker, we find the sparsity of data, the partial observability of the domain,…

This reference manual consists of two parts. The first (sections 1 through 6) is intended for those who are either interested in the ITS 1.5 time sharing monitor for its own sake or who wish to write machine language programs…

We report on a study of how people look for information within email, files, and the Web. When locating a document or searching for a specific answer, people relied on their contextual knowledge of their information target to help them…

This paper presents a novel algorithm for learning in a class of stochastic Markov decision processes (MDPs) with continuous state and action spaces that trades speed for accuracy. A transform of the stochastic MDP into a deterministic one is presented…

The development of increasingly sophisticated and powerful computers in the last few decades has frequently stimulated comparisons between them and the human brain. Such comparisons will become more earnest as computers are applied more and more to tasks formerly associated…