Title :
A framework for the credit-apportionment process in rule-based systems
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Michigan Univ., Ann Arbor, MI, USA
Abstract :
A framework for the credit-apportionment process is described. Such a framework is vital in studying the credit-apportionment problem because it provides a formal basis for the problem analysis and algorithm design. The framework includes: a system-environment model, which integrates the effects of payoffs with other relevant parts of a rule-based system to model various internal and external activities of the rule-based system; and principles of usefulness, which define the inherent usefulness of rule actions and provide the semantic aspect of the credit-apportionment process. This framework formulates the credit-apportionment problem as a problem of estimating and approximating the inherent usefulness from payoffs. Two conclusions from the framework are that: the usefulness of rule action is a function of the context in which the rule activates; and the scalar-valued rule strength is not adequate for the purpose of a credit-apportionment. These conclusions led to the development of a credit-apportionment algorithm, the context-array bucket-brigade algorithm
Keywords :
knowledge based systems; learning systems; IKBS; algorithm design; context-array bucket-brigade algorithm; credit-apportionment process; learning systems; principles of usefulness; problem analysis; rule actions; rule-based systems; scalar-valued rule strength; system-environment model; Algorithm design and analysis; Cybernetics; Feedback; Humans; Immune system; Intelligent systems; Knowledge based systems; Knowledge engineering; Problem-solving; Systems engineering and theory;
Journal_Title :
Systems, Man and Cybernetics, IEEE Transactions on