Domain structure and the complexity of diagnostic problem solving

• Authors: Thomas D. Wu
• Venue: AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
• Year: 1991

This paper provides a quantitative analysis of domain structure and its effects on the complexity of diagnostic problem solving. It introduces a hypothesis about the modular structure of domains and proposes a measured called explanatory power. The distribution of explanatory power reveals the inherent structure of domains. We conjecture that such structure might facilitate problem solving, even when the problem solver does not exploit it explicitly. To test this hypothesis, we create a domain without structure by randomizing the distribution of explanatory power. We use the structured and randomized knowledge bases to study the effect of domain structure on two diagnostic algorithms, candidate generation and symptom clustering. The results indicate that inherent domain structure, even when not encoded explicitly, can facilitate problem solving. Such facilitation occurs for both the candidate generation and symptom clustering algorithms. Moreover, domain structure appears to benefit symptom clustering more than candidate generation, suggesting that the efficiency of symptom clustering derives in part from exploiting domain structure.