Agenda control for heterogeneous reasoners

As artificial intelligence techniques are maturing and being deployed in large applications, the problem of specifying control and reasoning strategies is regaining attention. Complex AI systems tend to comprise a suite of modules, each of which is capable of solving a different aspect of the overall problem, and each of which may incorporate a different reasoning paradigm. The orchestration of such heterogeneous problem solvers can be divided into two subproblems: (1) When and how are various reasoning modes invoked? and (2) How is information passed between various reasoning modes? In this paper, we explore some solutions to this problem. In particular, we describe a logic programming system that is based on three ideas: equivalence of declarative and operational semantics, declarative specification of control information, and smoothness of interaction with non-logic-based programs. Meta-level predicates are used to specify control information declaratively, compensating for the absence of procedural constructs that usually facilitate formulation of efficient programs. Knowledge that has been derived in the course of the current inference process can at any time be passed to non-logic-based program modules. Traditional SLD inference engines maintain only the linear path to the current state in the SLD search tree: formulae that have been proved on this path are implicitly represented in a stack of recursive calls to the inference engine, and formulae that have been proved on previous, unsuccessful paths are lost altogether. In our system, previously proved formulae are maintained explicitly and therefore can be passed to other reasoning modules. As an application example, we show how this inference system acts as the knowledge representation and reasoning framework of Pret--a program that automates system identification.