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Stephen Beale

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3 papers

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AIIM Journal 2012 Journal Article

Inconsistency as a diagnostic tool in a society of intelligent agents

Marjorie McShane
Stephen Beale
Sergei Nirenburg
Bruce Jarrell
George Fantry

Objective To use the detection of clinically relevant inconsistencies to support the reasoning capabilities of intelligent agents acting as physicians and tutors in the realm of clinical medicine. Methods We are developing a cognitive architecture, OntoAgent, that supports the creation and deployment of intelligent agents capable of simulating human-like abilities. The agents, which have a simulated mind and, if applicable, a simulated body, are intended to operate as members of multi-agent teams featuring both artificial and human agents. The agent architecture and its underlying knowledge resources and processors are being developed in a sufficiently generic way to support a variety of applications. Results We show how several types of inconsistency can be detected and leveraged by intelligent agents in the setting of clinical medicine. The types of inconsistencies discussed include: test results not supporting the doctor's hypothesis; the results of a treatment trial not supporting a clinical diagnosis; and information reported by the patient not being consistent with observations. We show the opportunities afforded by detecting each inconsistency, such as rethinking a hypothesis, reevaluating evidence, and motivating or teaching a patient. Conclusions Inconsistency is not always the absence of the goal of consistency; rather, it can be a valuable trigger for further exploration in the realm of clinical medicine. The OntoAgent cognitive architecture, along with its extensive suite of knowledge resources an processors, is sufficient to support sophisticated agent functioning such as detecting clinically relevant inconsistencies and using them to benefit patient-centered medical training and practice.

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AAAI Conference 1997 Conference Paper

Using Branch-and-Bound with Constraint Satisfaction in Optimization Problems

Stephen Beale

This work1 integrates three related AI search techniques - constraint satisfaction, branch-and-bound and solution synthesis - and applies the result to constraint satisfaction problems for which optimal answers are required. This method has already been shown to work well in natural language semantic analysis (Beale, et al, 1996); here we extend the domain to optimizing graph coloring problems, which are abstractions of many common scheduling problems of interest. We demonstrate that the methods used here allow us to determine optimal answers to many types of problems without resorting to heuristic search, and, furthermore, can be combined with heuristic search methods for problems with excessive complexity.

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AAAI Conference 1996 Conference Paper

Hunter-Gatherer: Three Search Techniques Integrated for Natural Language Semantics

Stephen Beale

This work’ integrates three related AI search techniques - constraint satisfaction, branch-and-bound and solution synthesis - and applies the result to semantic processing in natural language (NL). We summarize the approach as “Hunter-Gatherer: ” o branch-and-bound and constraint satisfaction allow us to “hunt down” non-optimal and impossible solutions and prune them from the search space. e solution synthesis methods then “gather” all optimal solutions avoiding exponential complexity. Each of the three techniques is briefly described, as well as their extensions and combinations used in our system. We focus on the combination of solution synthesis and branch-and-bound methods which has enabled near-linear-time processing in our applications. Finally, we illustrate how the use of our technique in a large-scale MT project allowed a drastic reduction in search space.

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