著者

辻野 広司 ケルナー エドガー 桝谷 知彦

出版者

社団法人人工知能学会

雑誌

人工知能学会誌 (ISSN:09128085)

巻号頁・発行日

vol.12, no.3, pp.440-447, 1997-05-01

被引用文献数

6

---

We propose a multi-agent system for hypothetical reasoning based on a large-scale computational theory on essential characteristics of neocortical processing. For a problem-solving on a real world environment, we require both a large-scale computational theory and a robust local computational theory. As a large-scale computational theory, we develop a hypothetical reasoning system by introducing a knowledge-based control on agents and a local commu-nication among agents. These agents communicate each other to reach a globally consistent solution while they locally perform hypothesis generation, representation and evaluation based on a memory-based reasoning as a robust local computational theory. This memory-based reasoning is defined by a principal component analysis, and applies both a deductive reasoning and an inductive reasoning with a least amount of memory that are requisites for hypothetical reasoning. By its multiple representation of same-type knowledge, and its intrinsic local control for decision-state-dependent recall of that knowledge, the proposed agents also serve as symbolic representations of the signal description of a respective feature. Since vision is a typical case for problem-solving by hypothetical reasoning, the proposed general architecture has been used to implement a model on face recognition to verify its performance.