The challenge now facing AI is to produce systems exhibiting higher 'machine IQ' and using 'common sense' reasoning, rather than purely logical deduction. Soft computing is an attempt to meet this challenge, allowing computers to handle imprecision, uncertainty and partial truth. It has evolved from the success of fuzzy control and other area of 'sub-symbolic' artificial intelligence such as neural nets.
This book presents different perspectives on how to combine soft computing's tolerance of imprecision with logic programming's power and semantics to provide a way forward for the implementation of intelligent knowledge-based systems.

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