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A Comparative Analysis of Reinforcement Learning Methods
(1991-10-01)
This paper analyzes the suitability of reinforcement learning (RL) for both programming and adapting situated agents. We discuss two RL algorithms: Q-learning and the Bucket Brigade. We introduce a special case of the ...
Networks and the Best Approximation Property
(1989-10-01)
Networks can be considered as approximation schemes. Multilayer networks of the backpropagation type can approximate arbitrarily well continuous functions (Cybenko, 1989; Funahashi, 1989; Stinchcombe and White, 1989). We ...
Task-Level Robot Learning: Ball Throwing
(1987-12-01)
We are investigating how to program robots so that they learn tasks from practice. One method, task-level learning, provides advantages over simply perfecting models of the robot's lower level systems. Task-level ...
Learning Commonsense Categorical Knowledge in a Thread Memory System
(2004-05-18)
If we are to understand how we can build machines capable of broad purpose learning and reasoning, we must first aim to build systems that can represent, acquire, and reason about the kinds of commonsense knowledge that ...
Discovering Latent Classes in Relational Data
(2004-07-22)
We present a framework for learning abstract relational knowledge with the aimof explaining how people acquire intuitive theories of physical, biological, orsocial systems. Our approach is based on a generative relational ...
Learning a Color Algorithm from Examples
(1987-06-01)
We show that a color algorithm capable of separating illumination from reflectance in a Mondrian world can be learned from a set of examples. The learned algorithm is equivalent to filtering the image data---in which ...
Generating and Generalizing Models of Visual Objects
(1985-07-01)
We report on initial experiments with an implemented learning system whose inputs are images of two-dimensional shapes. The system first builds semantic network descriptions of shapes based on Brady's smoothed local ...
Learning by Augmenting Rules and Accumulating Censors
(1982-05-01)
This paper is a synthesis of several sets of ideas: ideas about learning from precedents and exercises, ideas about learning using near misses, ideas about generalizing if-then rules, and ideas about using censors to ...
Learning Physical Descriptions from Functional Definitions, Examples, and Precedents
(1982-11-01)
It is too hard to tell vision systems what things look like. It is easier to talk about purpose and what things are for. Consequently, we want vision systems to use functional descriptions to identify things when ...
Synthesis of Visual Modules from Examples: Learning Hyperacuity
(1991-01-01)
Networks that solve specific visual tasks, such as the evaluation of spatial relations with hyperacuity precision, can be eastily synthesized from a small set of examples. This may have significant implications for the ...