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ARIES is a generalized, approximate reasoning inference engine designed for easy incorporation as the deductive subsystem of a LISP-based information system. Inputs to ARIES are both conditioned (rules) and unconditioned (facts) propositions describing known aspects of the behavior and state of a physical system. The propositions represented and manipulated by ARIES are qualified as to their degree of truth, belief, likelihood, risk, or preference. In an ARIES application, several such truth qualifications may be specified and manipulated. Inference consists of the combination of these truth values according to rules prescribed by the user (or otherwise assigned by default by ARIES) that define the truth value of a proposition as a function of the truth values of other, related propositions. For such purposes, ARIES requires specification of formulas for the logical operations of conjunction, disjunction, and forward implication (modus ponens). The design of ARIES is very general and capable of supporting a wide variety of multivalued logic formalisms. The major emphasis of its implementation however, was on the ability to accommodate approaches, such as probability theory, where the truth-value of certain propositions (e.g., disjunction) cannot be defined as a numeric function of the truth value of the component propositions. For those non-truth-functional formalisms, ARIES provides capabilities to combine truth values that are only known in terms of user-specified numerical intervals where the unknown truth value lies. In its major mode of truth value manipulation ARIES computes the truth value of a user-given hypothesis (goal) by propagating truth intervals along inferential nets. The final output returned by ARIES as the (interval) truth value of the goal is the result of two optimizations in the AND-OR graph representing the rule domain. This paper discusses the rationale for the development of ARIES as an interval-oriented, optimization tool over inference nets and discusses the characteristics of its implementation in the Symbolics 3600 LISP Machine.
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