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,…

by Sherry L. Buschmann., Thesis (S.M.M.O.T.)--Massachusetts Institute of Technology, Sloan School of Management, Management of Technology Program, 2001., Includes bibliographical references (leaves 113-116).

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…

by Kah-Peng Aw., Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management, 2001., Includes bibliographical references (leaf 55).

This paper presents an adaptive learning model for market-making under the reinforcement learning framework. Reinforcement learning is a learning technique in which agents aim to maximize the long-term accumulated rewards. No knowledge of the market environment, such as the order…

Humans recognize optical reflectance properties of surfaces such as metal, plastic, or paper from a single image without knowledge of illumination. We develop a machine vision system to perform similar recognition tasks automatically. Reflectance estimation under unknown, arbitrary illumination proves…

We have developed a technique called RISE (Random Image Structure Evolution), by which one may systematically sample continuous paths in a high-dimensional image space. A basic RISE sequence depicts the evolution of an object's image from a random field, along…

All intelligence relies on search --- for example, the search for an intelligent agent's next action. Search is only likely to succeed in resource-bounded agents if they have already been biased towards finding the right answer. In artificial agents, the…

by Kimberly G. Waters., Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002., Includes bibliographical references (p. 45-46).