Brian E. Mastenbrook

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Based on several years of experience in generating code from large SDL and UML models in the telecommunications domain, it has become apparent that model analysis must be used to augment more traditional validation and testing techniques. While model correctness is extremely important, the difficulty of use and non-scalability of most formal verification(More)
Electronic document collections containing documents in machine-readable form lend themselves to attempts at automated indexing and classification. In fact, in many cases the size of these collections renders human indexing infeasible. Yet current automated indexing mechanisms analyze the textual content of documents and fall short of human indexing that(More)
In this paper we present a system that uses its underlying physiology, a hierarchical memory and a collection of memory management algorithms to learn concepts as cases and to build higher level concepts from experiences represented as sequences of atoms. Using a memory structure that requires all base memories to be grounded in introspective atoms, the(More)
Existing symbolic concept development systems create a concept tree from cases by specializing an infinitely general " root concept ". In these systems individual cases are assigned to only one concept (and all of its parents in the tree) and concepts themselves have only one direct parent. In this paper we present a symbolic concept development system(More)
In this paper we describe ASPARC, a system for independent formation of an agent's specific set of grounded primitive spatial concepts. Through all of its subsystems operating on a unified data structure , ASPARC's spatial memories can be manipulated and analyzed as encapsulated representations, used to form plans for reenacting events, or used as patterns(More)