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.

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This paper presents a comprehensive approach for model-based diagnosis which includes proposals for characterizing and computing preferred diagnoses, assuming that the system description is augmented with a system structure (a directed graph explicating the interconnections between system components). Specifically, we first introduce the notion of a consequence, which is a syntactically unconstrained propositional sentence that characterizes all consistency-based diagnoses and show that standard characterizations of diagnoses, such as minimal conflicts, correspond to syntactic variations on a consequence. Second, we propose a new syntactic variation on the consequence known as negation normal form (NNF) and discuss its merits compared to standard variations. Third, we introduce a basic algorithm for computing consequences in NNF given a structured system description. We show that if the system structure does not contain cycles, then there is always a linear--size consequence...

Our concern in this paper is with conjecturing diagnoses to explain what happened to a system, given a theory of system behaviour and some observed (aberrant) behaviour. We characterize what happened by introducing the notion of explanatory diagnoses in the language of the situation calculus. Explanatory diagnosesconjecture sequencesof actions to account for a change in system behaviour. We show that determining an explanatory diagnosis is analogous to the classical AI planning task. As such, we exploit previous results on goal regression in the situation calculus to show that determining an explanatory diagnosis can be achieved by regression followed by theorem proving in the database describing what is known of the initial state of our system. Further, we show that in the case of incomplete information, determining explanatory diagnoses is an abductive plan synthesis task.

We propose a model

In this paper we describe REVISE, an extended logic programming system for revising knowledge bases. REVISE is based on logic programming with explicit negation, plus a two-valued assumption revision to face contradiction , encompasses the notion of preference levels. Its reliance on logic programming allows efficient computation and declarativity, whilst its use of explicit negation, revision and preference levels enables modeling of a variety of problems including default reasoning, belief revision and modelbased reasoning. It has been implemented as a Prolog--meta interpreter and tested on a spate of examples, namely the representation of diagnosis strategies in modelbased reasoning systems.

This paper introduces the algorithm TREE DIAG for computing minimal diagnoses for tree structured systems. Diagnoses are computed by descending into the tree, enumerating the input combinations that might be reponsible for a given incorrect observation, and combining the diagnoses for the subtrees generating these inputs into diagnoses for the whole system. We prove soundness and correctness of the algorithm and show experimental results that indicate that it compares favorably to Reiter's hitting-set-based algorithm and El Fattah and Dechter's SAB. Extensions of the algorithm related to general acyclic systems, use of fault modes and the practical application to the software diagnosis domain are discussed. Keywords: Model-Based Diagnosis, Algorithms 1 Introduction Since the beginning of model-based diagnosis research, several attempts have been made to make model-based diagnosis of large systems feasible. This has been done by introducing probability measurements ([dK91]), by comput...

This paper provides a search-based algorithm for computing prior and posterior probabilities in discrete Bayesian Networks. This is an "anytime" algorithm, that at any stage can estimate the probabilities and give an error bound. Whereas the most popular Bayesian net algorithms exploit the structure of the network for efficiency, we exploit probability distributions for efficiency. The algorithm is most suited to the case where we have extreme (close to zero or one) probabilities, as is the case in many diagnostic situations where we are diagnosing systems that work most of the time, and for commonsense reasoning tasks where normality assumptions (allegedly) dominate. We give a characterisation of those cases where it works well, and discuss how well it can be expected to work on average. 1 Introduction This paper provides a general purpose search-based technique for computing posterior probabilities in arbitrarily structured discrete 1 Bayesian networks. Implementations of Bayesia...

Several artificial intelligence architectures and systems based on "deep" models of a domain have been proposed, in particular for the diagnostic task. These systems have several advantages over traditional knowledge based systems, but they have a main limitation in their computational complexity. One of the ways to face this problem is to rely on a knowledge compilation phase, which produces knowledge that can be used more effectively with respect to the original one. In this paper we show how a specific knowledge compilation approach can focus reasoning in abductive diagnosis, and, in particular, can improve the performances of AID, an abductive diagnosis system. The approach aims at focusing the overall diagnostic cycle in two interdependent ways: avoiding the generation of candidate solutions to be discarded a-posteriori and integrating the generation of candidate solutions with discrimination among different candidates. Knowledge compilation is used off-line to produce operational...

This paper describes a diagnosis algorithm called structure-based abduction (SAB) which was developed in the framework of constraint networks [ 12 ] . The algorithm exploits the structure of the constraint network and is most efficient for near-tree problem domains. By analyzing the structure of the problem domain, the performance of such algorithms can be bounded in advance. We present empirical results comparing SAB with two modelbased algorithms, MBD1 and MBD2, for the task of finding one or all minimal-cardinality diagnoses. MBD1 uses the same computing strategy as algorithm GDE [ 9 ] . MBD2 adopts a breadth-first search strategy similar to the algorithm DIAGNOSE [ 24 ] . The main conclusion is that for nearly acyclic circuits, such as the N-bit adder, the performance of SAB being linear provides definite advantages as the size of the circuit increases. 1 Introduction Generally speaking, diagnosis is a form of abduction or inference to the best explanation. Exp...