We introduce a greedy local search procedure called GSAT for solving propositional satisfiability problems. Our experiments show that this procedure can be used to solve hard, randomly generated problems that are an order of magnitude larger than those that can be handled by more traditional approaches such as the Davis-Putnam procedure or resolution. We also show that GSAT can solve structured satisfiability problems quickly. In particular, we solve encodings of graph coloring problems, N-queens, and Boolean induction. General application strategies and limitations of the approach are also discussed. GSAT is best viewed as a model-finding procedure. Its good performance suggests that it may be advantageous to reformulate reasoning tasks that have traditionally been viewed as theorem-proving problems as model-finding tasks.

This paper presents a simple framework for Horn-clause abduction, with probabilities associated with hypotheses. The framework incorporates assumptions about the rule base and independence assumptions amongst hypotheses. It is shown how any probabilistic knowledge representable in a discrete Bayesian belief network can be represented in this framework. The main contribution is in finding a relationship between logical and probabilistic notions of evidential reasoning. This provides a useful representation language in its own right, providing a compromise between heuristic and epistemic adequacy. It also shows how Bayesian networks can be extended beyond a propositional language. This paper also shows how a language with only (unconditionally) independent hypotheses can represent any probabilistic knowledge, and argues that it is better to invent new hypotheses to explain dependence rather than having to worry about dependence in the language. Scholar, Canadian Institute for Advanced...

In this paper, we review recent work aimed at the application of declarative logic programming to knowledge representation in artificial intelligence. We consider exten- sions of the language of definite logic programs by classical (strong) negation, disjunc- tion, and some modal operators and show how each of the added features extends the representational power of the language.

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.

Recently several local hill-climbing procedures for propositional satisability havebeen proposed, which are able to solve large and di cult problems beyond the reach ofconventional algorithms like Davis-Putnam. By the introduction of some new variants of these procedures, we provide strong experimental evidence to support the conjecture that neither greediness nor randomness is important in these procedures. One of the variants introduced seems to o er signi cant improvements over earlier procedures. In addition, we investigate experimentally how their performance depends on their parameters. Our results suggest that run-time scales less than simply exponentially in the problem size. 1

This paper investigates two different activities that involve making assumptions: predicting what one expects to be true and explaining observations. In a companion paper, an architecture for both prediction and explanation is proposed and an implementation is outlined. In this paper, we show how such a hypothetical reasoning system can be used to solve recognition, diagnostic and prediction problems. As part of this is the assumption that the default reasoner must be "programmed" to get the right answer and it is not just a matter of "stating what is true" and hoping the system will magically find the right answer. A number of distinctions have been found in practice to be important: between predicting whether something is expected to be true versus explaining why it is true; and between conventional defaults (assumptions as a communication convention), normality defaults (assumed for expediency) and conjectures (assumed only if there is evidence). The effects of these distinctions on...

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Good Old Fashioned Artificial Intelligence and Robotics (GOFAIR) relies on a set of restrictive Omniscient Fortune Teller Assumptions about the agent, the world and their relationship. The emerging Situated Agent paradigm is challenging GOFAIR by grounding the agent in space and time, relaxing some of those assumptions, proposing new architectures and integrating perception, reasoning and action in behavioral modules. GOFAIR is typically forced to adopt a hybrid architecture for integrating signal-based and symbol-based approaches because of the inherent mismatch between the corresponding on-line and off-line computational models. It is argued that Situated Agents should be designed using a unitary on-line computational model. The Constraint Net model of Zhang and Mackworth satisfies that requirement. Two systems for situated perception built in our laboratory are described to illustrate the new approach: one for visual monitoring of a robot's arm, the other for real-time visual control of multiple robots competing and cooperating in a dynamic world.

Understanding observations of interacting objects requires one to reason about the force-dynamic relations between objects. We present an implemented computational theory that derives force-dynamic interpretations directly from camera input. Interpretations are expressed in terms of assertions about the kinematic and dynamic properties of objects. The feasibility of interpretations can be determined relative to Newtonian mechanics by a reduction to linear programming. Multiple feasible solutions are compared using a preference hierarchy to select plausible interpretations. We provide computational examples to demonstrate that our ontology is sufficiently rich to describe a wide variety of image sequences. KEYWORDS: Motion understanding, Scene dynamics, Perceptual inference, Knowledgebased perception, Domain theory, View-based representations. Submitted. 1 Introduction Both AI and psychology researchers have argued for the need to represent "causal" information about the world in ...

Although the computational perspective on cognitive tasks has always played a major role in Artificial Intelligence, the interest in the precise determination of the computational costs that are required for solving typical AI problems has grown only recently. In this paper, we will describe what insights a computational complexity analysis can provide and what methods are available to deal with the complexity problem. This work was partially supported by the European Commission as part of DRUMS-II, the ESPRIT Basic Research Project P6156. 1 Introduction It is well-known that typical AI problems, such as natural language understanding, scene interpretation, planning, configuration, or diagnosis are computationally difficult. Hence, it seems to be worthless to analyze the computational complexity of these problems. In fact, some people believe that all AI problems are NP-hard or even undecidable. Conceiving AI as a scientific field that has as its goal the analysis and synthesis of...