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A Logical Approach to HighLevel Robot Programming  A Progress Report
 IN CONTROL OF THE PHYSICAL WORLD BY INTELLIGENT SYSTEMS: PAPERS FROM THE 1994 AAAI FALL SYMPOSIUM
, 1994
"... This paper describes a novel approach to highlevel robot programming based on a highly developed logical theory of action. The user provides a specification of the robot's basic actions (their preconditions and effects on the environment) as well as of relevant aspects of the environment, ..."
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Cited by 94 (16 self)
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This paper describes a novel approach to highlevel robot programming based on a highly developed logical theory of action. The user provides a specification of the robot's basic actions (their preconditions and effects on the environment) as well as of relevant aspects of the environment, in an extended version of the situation calculus. He can then specify robot behaviors in terms of these actions in a programming language that allows references to world conditions (e.g. if 9c(Pop can(c) On table(c)) then pick up(c)). The programs can be executed to drive the robot. The interpreter automatically maintains the world model required to execute programs based on the specification. The theoretical framework includes a solution to the frame problem and is very general  it handles dynamic and incompletely known worlds, as well as perception actions. Given this kind of domain specification, it is also possible to support more sophisticated reasoning, such as task planni...
Plausibility Measures and Default Reasoning
 Journal of the ACM
, 1996
"... this paper: default reasoning. In recent years, a number of different semantics for defaults have been proposed, such as preferential structures, fflsemantics, possibilistic structures, and rankings, that have been shown to be characterized by the same set of axioms, known as the KLM properties. W ..."
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Cited by 81 (12 self)
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this paper: default reasoning. In recent years, a number of different semantics for defaults have been proposed, such as preferential structures, fflsemantics, possibilistic structures, and rankings, that have been shown to be characterized by the same set of axioms, known as the KLM properties. While this was viewed as a surprise, we show here that it is almost inevitable. In the framework of plausibility measures, we can give a necessary condition for the KLM axioms to be sound, and an additional condition necessary and sufficient to ensure that the KLM axioms are complete. This additional condition is so weak that it is almost always met whenever the axioms are sound. In particular, it is easily seen to hold for all the proposals made in the literature. Categories and Subject Descriptors: F.4.1 [Mathematical Logic and Formal Languages]:
Probabilistic Reasoning in Terminological Logics
, 1994
"... In this paper a probabilistic extensions for terminological knowledge representation languages is defined. Two kinds of probabilistic statements are introduced: statements about conditional probabilities between concepts and statements expressing uncertain knowledge about a specific object. The usua ..."
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Cited by 78 (5 self)
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In this paper a probabilistic extensions for terminological knowledge representation languages is defined. Two kinds of probabilistic statements are introduced: statements about conditional probabilities between concepts and statements expressing uncertain knowledge about a specific object. The usual modeltheoretic semantics for terminological logics are extended to define interpretations for the resulting probabilistic language. It is our main objective to find an adequate modelling of the way the two kinds of probabilistic knowledge are combined in commonsense inferences of probabilistic statements. Cross entropy minimization is a technique that turns out to be very well suited for achieving this end. 1 INTRODUCTION Terminological knowledge representation languages (concept languages, terminological logics) are used to describe hierarchies of concepts. While the expressive power of the various languages that have been defined (e.g. KLONE [BS85] ALC [SSS91]) varies greatly in that ...
Learning to reason
 Journal of the ACM
, 1994
"... Abstract. We introduce a new framework for the study of reasoning. The Learning (in order) to Reason approach developed here views learning as an integral part of the inference process, and suggests that learning and reasoning should be studied together. The Learning to Reason framework combines the ..."
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Cited by 57 (24 self)
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Abstract. We introduce a new framework for the study of reasoning. The Learning (in order) to Reason approach developed here views learning as an integral part of the inference process, and suggests that learning and reasoning should be studied together. The Learning to Reason framework combines the interfaces to the world used by known learning models with the reasoning task and a performance criterion suitable for it. In this framework, the intelligent agent is given access to its favorite learning interface, and is also given a grace period in which it can interact with this interface and construct a representation KB of the world W. The reasoning performance is measured only after this period, when the agent is presented with queries � from some query language, relevant to the world, and has to answer whether W implies �. The approach is meant to overcome the main computational difficulties in the traditional treatment of reasoning which stem from its separation from the “world”. Since the agent interacts with the world when constructing its knowledge representation it can choose a representation that is useful for the task at hand. Moreover, we can now make explicit the dependence of the reasoning performance on the environment the agent interacts with. We show how previous results from learning theory and reasoning fit into this framework and
Modeling Agents as Qualitative Decision Makers
 Artificial Intelligence
, 1997
"... We investigate the semantic foundations of a method for modeling agents as entities with a mental state which was suggested by McCarthy and by Newell. Our goals are to formalize this modeling approach and its semantics, to understand the theoretical and practical issues that it raises, and to addres ..."
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Cited by 51 (0 self)
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We investigate the semantic foundations of a method for modeling agents as entities with a mental state which was suggested by McCarthy and by Newell. Our goals are to formalize this modeling approach and its semantics, to understand the theoretical and practical issues that it raises, and to address some of them. In particular, this requires specifying the model's parameters and how these parameters are to be assigned (i.e., their grounding). We propose a basic model in which the agent is viewed as a qualitative decision maker with beliefs, preferences, and decision strategy; and we show how these components would determine the agent's behavior. We ground this model in the agent's interaction with the world, namely, in its actions. This is done by viewing model construction as a constraint satisfaction problem in which we search for a model consistent with the agent's behavior and with our general background knowledge. In addition, we investigate the conditions under which a mental st...
Probabilistic logic and probabilistic networks
, 2008
"... While in principle probabilistic logics might be applied to solve a range of problems, in practice they are rarely applied at present. This is perhaps because they seem disparate, complicated, and computationally intractable. However, we shall argue in this programmatic paper that several approaches ..."
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Cited by 19 (15 self)
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While in principle probabilistic logics might be applied to solve a range of problems, in practice they are rarely applied at present. This is perhaps because they seem disparate, complicated, and computationally intractable. However, we shall argue in this programmatic paper that several approaches to probabilistic logic fit into a simple unifying framework: logically complex evidence can be used to associate probability intervals or probabilities with sentences. Specifically, we show in Part I that there is a natural way to present a question posed in probabilistic logic, and that various inferential procedures provide semantics for that question: the standard probabilistic semantics (which takes probability functions as models), probabilistic argumentation (which considers the probability of a hypothesis being a logical consequence of the available evidence), evidential probability (which handles reference classes and frequency data), classical statistical inference
Explanation Closure, Action Closure, and the Sandewall Test Suite for Reasoning about Change
 Journal of Logic and Computation
, 1992
"... Explanation closure (EC) axioms were previously introduced as a means of solving the frame problem. This paper provides a thorough demonstration of the power of EC combined with action closure (AC) for reasoning about dynamic worlds, by way of Sandewall's test suite of 12orso problems [2931] ..."
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Cited by 17 (2 self)
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Explanation closure (EC) axioms were previously introduced as a means of solving the frame problem. This paper provides a thorough demonstration of the power of EC combined with action closure (AC) for reasoning about dynamic worlds, by way of Sandewall's test suite of 12orso problems [2931]. Sandewall's problems range from the "Yale turkey shoot" (and variants) to the "stuffy room" problem, and were intended as a test and challenge for nonmonotonic logics of action. The EC/ACbased solutions for the most part do not resort to nonmonotonic reasoning at all, yet yield the intuitively warranted inferences in a direct, transparent fashion. While there are good reasons for ultimately employing nonmonotonic or probabilistic logics  e.g., pervasive uncertainty and the qualification problem  this does show that the scope of monotonic methods has been underestimated. Subsidiary purposes of the paper are to clarify the intuitive status of EC axioms in relation to action effect axioms; and to show how EC, previously formulated within the situation calculus, can be applied within the framework of a temporal logic similar to Sandewall's "discrete uent logic", with some gains in clarity.
A Modal Logic for Subjective Default Reasoning
, 1994
"... In this paper we introduce DML: Default Modal Logic. DML is a logic endowed with a twoplace modal connective that has the intended meaning of "If ff, then normally fi". On top of providing a welldefined tool for analyzing common default reasoning, DML allows nesting of the default op ..."
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Cited by 17 (0 self)
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In this paper we introduce DML: Default Modal Logic. DML is a logic endowed with a twoplace modal connective that has the intended meaning of "If ff, then normally fi". On top of providing a welldefined tool for analyzing common default reasoning, DML allows nesting of the default operator. We present a semantic framework in which many of the known default proof systems can be naturally characterized, and prove soundness and completeness theorems for several such proof systems. Our semantics is a "neighbourhood modal semantics", and it allows for subjective defaults, that is, defaults may vary among different worlds within the same model. The semantics has an appealing intuitive interpretation and may be viewed as a settheoretic generalization of the probabilistic interpretations of default reasoning. We show that our semantics is most general in the sense that any modal semantics that is sound for some basic axioms for default reasoning is a special case of our semantics. Such a generality result may serve to provide a semantical analysis of the relative strength of different proof systems and to show the nonexistence of semantics with certain properties. 2 1
Learning to Reason: The NonMonotonic Case
, 1995
"... We suggest a new approach for the study of the nonmonotonicity of human commonsense reasoning. The two main premises that underlie this work are that commonsense reasoning is an inductive phenomenon, and that missing information in the interaction of the agent with the environment may be as informat ..."
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Cited by 15 (8 self)
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We suggest a new approach for the study of the nonmonotonicity of human commonsense reasoning. The two main premises that underlie this work are that commonsense reasoning is an inductive phenomenon, and that missing information in the interaction of the agent with the environment may be as informative for future interactions as observed information. This intuition is formalized and the problem of reasoning from incomplete information is presented as a problem of learning attribute functions over a generalized domain. We consider examples that illustrate various aspects of the nonmonotonic reasoning phenomena, which have been used over the years as "benchmarks" for various formalisms, and translate them into Learning to Reason problems. We demonstrate that these have concise representations over the generalized domain and prove that these representations can be learned efficiently. The framework developed suggests an "operational " approach to studying reasoning that is nevertheless ...