Much Learning is done by way of studying precedents and exercises. A teacher supplies a story, gives a problem, and expects a student both to solve a problem and to discover a principle. The student must find the correspondence between…

The next generation of artificial intelligence programs will require the ability to organize knowledge as groups of active objects. Each object should have only its own local expertise, the ability to operate in parallel with other objects, and the ability…

As advances in computer architecture and changing economics make feasible machines with large-scale parallelism, Artificial Intelligence will require new ways of thinking about computation that can exploit parallelism effectively. We present the actor model of computation as being appropriate for…

This paper describes a natural language system START. The system analyzes English text and automatically transforms it into an appropriate representation, the knowledge base, which incorporates the information found in the text. The user gains access to information stored in…

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 Bucket Brigade, and analyze and compare its…

We develop a novel autonomous control synthesis strategy called Phase Space Navigator for the automatic synthesis of nonlinear control systems. The Phase Space Navigator generates global control laws by synthesizing flow shapes of dynamical systems and planning and navigating system…

We develop a qualitative method for understanding and representing phase space structures of complex systems and demonstrate the method with a program, MAPS --- Modeler and Analyzer for Phase Spaces, using deep domain knowledge of dynamical system theory. Given a…

Most knowledge representation languages are based on classes and taxonomic relationships between classes. Taxonomic hierarchies without defaults or exceptions are semantically equivalent to a collection of formulas in first order predicate calculus. Although designers of knowledge representation languages often express…

A key problem in model-based object recognition is selection, namely, the problem of determining which regions in the image are likely to come from a single object. In this paper we present an approach that extracts and uses color region…

Combining numerical techniques with ideas from symbolic computation and with methods incorporating knowledge of science and mathematics leads to a new category of intelligent computational tools for scientists and engineers. These tools autonomously prepare simulation experiments from high-level specifications of…