Renewed motives for space exploration have inspired NASA to work toward the goal of establishing a virtual presence in space, through heterogeneous effets of robotic explorers. Information technology, and Artificial Intelligence in particular, will play a central role in this endeavor by endowing these explorers with a form of computational intelligence that we call remote agents. In this paper we describe the Remote Agent, a specific autonomous agent architecture based on the principles of model-based programming, on-board deduction and search, and goal-directed closed-loop commanding, that takes a significant step toward enabling this future. This architecture addresses the unique characteristics of the spacecraft domain that require highly reliable autonomous operations over long periods of time with tight deadlines, resource constraints, and concurrent activity among tightly coupled subsystems. The Remote Agent integrates constraint-based temporal planning and scheduling, robust multi-threaded execution, and model-based mode identification and reconfiguration. The demonstration of the integrated system as an on-board controller for Deep Space One, NASA's rst New Millennium mission, is scheduled for a period of a week in late 1998. The development of the Remote Agent also provided the opportunity to reassess some of AI's conventional wisdom about the challenges of implementing embedded systems, tractable reasoning, and knowledge representation. We discuss these issues, and our often contrary experiences, throughout the paper.

Computational efficiency is a central concern in the design of knowledge representation systems. In order to obtain efficient systems, it has been suggested that one should limit the form of the statements in the knowledge base or use an incomplete inference mechanism. The former approach is often too restrictive for practical applications, whereas the latter leads to uncertainty about exactly what can and cannot be inferred from the knowledge base. We present a third alternative, in which knowledge given in a general representation language is translated (compiled) into a tractable form — allowing for efficient subsequent query answering. We show how propositional logical theories can be compiled into Horn theories that approximate the original information. The approximations bound the original theory from below and above in terms of logical strength. The procedures are extended to other tractable languages (for example, binary clauses) and to the first-order case. Finally, we demonstrate the generality of our approach by compiling concept descriptions in a general framebased language into a tractable form.

. The satisfiability (SAT) problem is a core problem in mathematical logic and computing theory. In practice, SAT is fundamental in solving many problems in automated reasoning, computer-aided design, computeraided manufacturing, machine vision, database, robotics, integrated circuit design, computer architecture design, and computer network design. Traditional methods treat SAT as a discrete, constrained decision problem. In recent years, many optimization methods, parallel algorithms, and practical techniques have been developed for solving SAT. In this survey, we present a general framework (an algorithm space) that integrates existing SAT algorithms into a unified perspective. We describe sequential and parallel SAT algorithms including variable splitting, resolution, local search, global optimization, mathematical programming, and practical SAT algorithms. We give performance evaluation of some existing SAT algorithms. Finally, we provide a set of practical applications of the sat...

We present procedures to compile any propositional clausal database \Sigma into a logically equivalent "compiled" database \Sigma ? such that, for any clause C, \Sigma j= C if and only if there is a unit refutation of \Sigma ? [ :C. It follows that once the compilation process is complete any query about the logical consequences of \Sigma can be correctly answered in time linear in the sum of the sizes of \Sigma ? and the query. The compiled database \Sigma ? is for all but one of the procedures a subset of the set P I (\Sigma) of prime implicates of \Sigma, but \Sigma ? can be exponentially smaller than P I (\Sigma). Of independent interest, we prove the equivalence of unit-refutability with two restrictions of resolution, and provide a new sufficient condition for unit refutation completeness, thus identifying a new class of tractable theories, one which is of interest to abduction problems as well. Finally, we apply the results to the design of a complete LTMS. 1 INTRODUCT...

The satisfiability problem (SAT) is a fundamental problem in mathematical logic, constraint satisfaction, VLSI engineering, and computing theory. Methods to solve the satisfiability problem play an important role in the development of computing theory and systems. Traditional methods treat the SAT problem as a constrained decision problem. During past research, the number of unsatisfiable clauses as the value of an objective function was formulated. This transforms the SAT problem into a search problem --- an unconstrained optimization problem to the objective function. A variety of iterative optimization techniques can be used to solve this optimization problem. In this paper, we show how to use local search techniques to solve the SAT problem. The average time complexity analysis and numerous real algorithm executions were performed. They indicate that the local search algorithms are much more efficient than the existing SAT algorithms for certain classes of conjunctive normal form (...

The trend to increasingly capable and affordable control processors has generated an explosion of embedded real-time gadgets that serve almost every function imaginable. The daunting task of programming these gadgets is greatly alleviated with real-time deductive engines that perform all execution and monitoring functions from a single core model. Fast response times are achieved using an incremental propositional deductive database (an LTMS). Ideally the cost of an LTMS's incremental update should be linear in the number of labels that change between successive contexts. Unfortunately an LTMS can expend a significant percentage of its time working on labels that remain constant between contexts. This is caused by the LTMS's conservative approach: a context switch first removes all consequences of deleted clauses, whether or not those consequences hold in the new context. This paper presents a more aggressive incremental TMS, called the ITMS, that avoids processing a significant number...

The satisfiability (SAT) problem is a fundamental problem in mathematical logic, inference, automated reasoning, VLSI engineering, and computing theory. In this paper, following CNF and DNF local search methods, we introduce the Universal SAT problem model, UniSAT, that transforms the discrete SAT problem on Boolean space f0; 1g m into an unconstrained global optimization problem on real space E m . A direct correspondence between the solution of the SAT problem and the global minimum point of the UniSAT objective function is established. Many existing global optimization algorithms can be used to solve the UniSAT problems. Combined with backtracking /resolution procedures, a global optimization algorithm is able to verify satisfiability as well as unsatisfiability. This approach achieves significant performance improvements for certain classes of conjunctive normal form (CNF ) formulas. It offers a complementary approach to the existing SAT algorithms.

This dissertation addresses some problems raised by the well-known intractability of deductive reasoning in even moderately expressive knowledge representation systems. Starting from

Prime implicants/implicates (PIs) have been shown to be a useful tool in several problem domains. In Model-Based Diagnosis (MBD), [de Kleer et al. 90] have used PIs to characterize diagnoses. We present a PI generation algorithm which, although based on the general SE-tree-based search framework, is effectively an improvement of a particular PI generation algorithm proposed by [Slagle et al. 70]. The improvement is achieved via a decomposition tactic which is boosted by the SE-tree-based framework. The new algorithm is also more flexible in a number of ways. We present empirical results comparing the new algorithm to the old one, as well as to current PI generation algorithms. 1 Introduction Prime implicates/implicants (PIs) were a topic of great interest to researchers in the early days of computer science, in part because of their use in procedures for boolean function minimization [Quine 52]. A number of algorithms were developed, including [Quine 52], [Karnaugh 53], [McCluskey 56]...

We draw a simple correspondence between kernel rules and prime implicants. Kernel (minimal) rules play an important role in many induction techniques. Prime implicants were previously used to formally model many other problem domains, including Boolean circuit minimization and such classical AI problems as diagnosis, truth maintenance and circumscription. This correspondence allows computing kernel rules using any of a number of prime implicant generation algorithms. It also leads us to an algorithm in which learning is boosted by an auxiliary domain theory, e.g., a set of rules provided by an expert, or a functional description of a device or system; we discuss this algorithm in the context of SE-tree-based generation of prime implicants. Introduction Rules have always played an important role in Artificial Intelligence (AI). In machine learning, while a variety of other representations have also been used, a great deal of research has focused on rule induction. Moreover, many of the...