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215
Sound and efficient closedworld reasoning for planning
 Artificial Intelligence
, 1997
"... Closedworld inference is the process of determining that a logical sentence is false based on its absence from a knowledge base, or the inability to derive it. This process is essential for planning with incomplete information. We describe a novel method for closedworld inference and update over t ..."
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Cited by 88 (13 self)
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Closedworld inference is the process of determining that a logical sentence is false based on its absence from a knowledge base, or the inability to derive it. This process is essential for planning with incomplete information. We describe a novel method for closedworld inference and update over the firstorder theories of action used by planning algorithms such as NONLIN, TWEAK, and UCPOP. We show the method to be sound and efficient, but incomplete. In our experiments, closedworld inference consistently averaged about 2 milliseconds, while updates averaged approximately 1.2 milliseconds. We incorporated the method into the XII planner, which supports our Internet Softbot (software robot). The method cut the number of actions executed by the Softbot bya factor of one hundred, and resulted in a corresponding speedup to XII. 1
On the Semantics of Arbitration
 International Journal of Algebra and Computation
, 1995
"... : Revision and update operators add new information to some old information represented by a logical theory. Katsuno and Mendelzon show that both revision and update operators can be characterized as accomplishing a minimal change in the old information to accommodate the new information. Arbitratio ..."
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Cited by 87 (4 self)
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: Revision and update operators add new information to some old information represented by a logical theory. Katsuno and Mendelzon show that both revision and update operators can be characterized as accomplishing a minimal change in the old information to accommodate the new information. Arbitration operators add two or more weighted informations together where the weights indicate the relative importance of the informations rather than a strict priority. This paper shows that arbitration operators can be also characterized as accomplishing a minimal change. The operator of modelfitting is also defined and analyzed in the paper. 1 Introduction Arbitration is the process of settling a conflict between two or more persons. Arbitration occurs in many situations. For example, settling a labor dispute by an outsider, reaching a verdict in a trial, evaluating several alternative research hypotheses, negotiating an international peace agreement, or setting the price of a product in a compe...
On properties of update sequences based on causal rejection
 JOURNAL OF THE THEORY AND PRACTICE OF LOGIC PROGRAMMING
, 2002
"... In this paper, we consider an approach to update nonmonotonic knowledge bases represented as extended logic programs under the answer set semantics. In this approach, new information is incorporated into the current knowledge base subject to a causal rejection principle, which enforces that, in case ..."
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Cited by 76 (13 self)
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In this paper, we consider an approach to update nonmonotonic knowledge bases represented as extended logic programs under the answer set semantics. In this approach, new information is incorporated into the current knowledge base subject to a causal rejection principle, which enforces that, in case of conflicts between rules, more recent rules are preferred and older rules are overridden. Such a rejection principle is also exploited in other approaches to update logic programs, notably in the method of dynamic logic programming, due to Alferes et al. One of the central issues of this paper is a thorough analysis of various properties of the current approach, in order to get a better understanding of the inherent causal rejection principle. For this purpose, we review postulates and principles for update and revision operators which have been proposed in the area of theory change and nonmonotonic reasoning. Moreover, some new properties for approaches to updating logic programs are considered as well. Like related update approaches, the current semantics does not incorporate a notion of minimality of change, so we consider refinements of the semantics in this direction. As well, we investigate the relationship of our approach to others in more detail. In particular, we show that the current approach is semantically equivalent to inheritance programs, which have been independently defined by Buccafurri et al., and that it coincides with certain classes of dynamic logic programs. In view of this analysis, most of our results about properties of the causal rejection principle apply to each of these approaches as well. Finally, we also deal with computational issues. Besides a discussion on the computational complexity of our approach, we outline how the update semantics and its refinements can be directly implemented on top of existing logic programming systems. In the present case, we implemented the update approach using the logic programming system DLV.
The comparative linguistics of knowledge representation
 In Proc. of IJCAI’95
, 1995
"... We develop a methodology for comparing knowledge representation formalisms in terms of their "representational succinctness, " that is, their ability to express knowledge situations relatively efficiently. We use this framework for comparing many important formalisms for knowledge base rep ..."
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Cited by 70 (2 self)
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We develop a methodology for comparing knowledge representation formalisms in terms of their "representational succinctness, " that is, their ability to express knowledge situations relatively efficiently. We use this framework for comparing many important formalisms for knowledge base representation: propositional logic, default logic, circumscription, and model preference defaults; and, at a lower level, Horn formulas, characteristic models, decision trees, disjunctive normal form, and conjunctive normal form. We also show that adding new variables improves the effective expressibility of certain knowledge representation formalisms. 1
On the Semantics of Theory Change: Arbitration between Old and New Information
 In Proceedings of the Twelfth ACM SIGACTSIGMODSIGART Symposium on Principles of Databases
, 1993
"... : Katsuno and Mendelzon divide theory change, the problem of adding new information to a logical theory, into two types: revision and update. We propose a third type of theory change: arbitration. The key idea is the following: the new information is considered neither better nor worse than the old ..."
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Cited by 63 (0 self)
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: Katsuno and Mendelzon divide theory change, the problem of adding new information to a logical theory, into two types: revision and update. We propose a third type of theory change: arbitration. The key idea is the following: the new information is considered neither better nor worse than the old information represented by the logical theory. The new information is simply one voice against a set of others already incorporated into the logical theory. From this follows that arbitration should be commutative. First we define arbitration by a set of postulates and then describe a modeltheoretic characterization of arbitration for the case of propositional logical theories. We also study weighted arbitration where different models of a theory can have different weights. 1 Introduction The problem of updating logical theories is a common fundamental concern to databases, to Artificial Intelligence [McC68, Rei92], and to belief revision [Mak85, Gar88]. It is wellknown that giving semant...
Propositional Belief Base Update and Minimal Change
 Artificial Intelligence
, 1999
"... In this paper we examine ten concrete propositional update operations of the literature. We start by completely characterizing their relative strength and their computational complexity. Then we evaluate the competing update operations w.r.t. the postulates proposed by Katsuno and Mendelzon. It turn ..."
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Cited by 60 (6 self)
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In this paper we examine ten concrete propositional update operations of the literature. We start by completely characterizing their relative strength and their computational complexity. Then we evaluate the competing update operations w.r.t. the postulates proposed by Katsuno and Mendelzon. It turns out that the majority violates most of the postulates. We argue that all violated postulates are undesirable except one. After that we evaluate the update operations w.r.t. another property which has been investigated extensively in the literature, viz. that disjunctive updates should not be identified with the exclusive disjunction. We argue that this is desirable, and show that the argument gives further support to the rejection of two of the postulates. Finally we study how the different approaches accommodate general laws governing the world, alias integrity constraints. Summing up our results, we conclude that only two of the update operations are satisfactory. Key words: Belief chan...
On the Tractable Counting of Theory Models and its Application to Truth Maintenance and Belief Revision
 Journal of Applied NonClassical Logics
, 2000
"... We address the problem of counting the models of a propositional theory, under incremental changes to the theory. Specifically, we show that if a propositional theory is in a special form that we call smooth, deterministic, decomposable negation normal form (sdDNNF), then for any consistent set of ..."
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Cited by 58 (18 self)
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We address the problem of counting the models of a propositional theory, under incremental changes to the theory. Specifically, we show that if a propositional theory is in a special form that we call smooth, deterministic, decomposable negation normal form (sdDNNF), then for any consistent set of literals S, we can simultaneously count, in time linear in the size of , the models of: [ S; [ S [ flg: for every literal l 62 S; [ S n flg: for every literal l 2 S; [ S n flg [ f:lg: for every literal l 2 S.
Learning partially observable deterministic action models
 In Proc. Nineteenth International Joint Conference on Artificial Intelligence (IJCAI ’05
, 2005
"... We present exact algorithms for identifying deterministicactions ’ effects and preconditions in dynamic partially observable domains. They apply when one does not know the action model (the way actions affect the world) of a domain and must learn it from partial observations over time. Such scenari ..."
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Cited by 55 (2 self)
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We present exact algorithms for identifying deterministicactions ’ effects and preconditions in dynamic partially observable domains. They apply when one does not know the action model (the way actions affect the world) of a domain and must learn it from partial observations over time. Such scenarios are common in real world applications. They are challenging for AI tasks because traditional domain structures that underly tractability (e.g., conditional independence) fail there (e.g., world features become correlated). Our work departs from traditional assumptions about partial observations and action models. In particular, it focuses on problems in which actions are deterministic of simple logical structure and observation models have all features observed with some frequency. We yield tractable algorithms for the modified problem for such domains. Our algorithms take sequences of partial observations over time as input, and output deterministic action models that could have lead to those observations. The algorithms output all or one of those models (depending on our choice), and are exact in that no model is misclassified given the observations. Our algorithms take polynomial time in the number of time steps and state features for some traditional action classes examined in the AIplanning literature, e.g., STRIPS actions. In contrast, traditional approaches for HMMs and Reinforcement Learning are inexact and exponentially intractable for such domains. Our experiments verify the theoretical tractability guarantees, and show that we identify action models exactly. Several applications in planning, autonomous exploration, and adventuregame playing already use these results. They are also promising for probabilistic settings, partially observable reinforcement learning, and diagnosis. 1.
The Size of a Revised Knowledge Base
 Artificial Intelligence
, 1995
"... In this paper we address a specific computational aspect of belief revision: The size of the propositional formula obtained by means of the revision of a formula with a new one. In particular, we focus on the size of the smallest formula equivalent to the revised knowledge base. The main result of t ..."
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Cited by 46 (19 self)
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In this paper we address a specific computational aspect of belief revision: The size of the propositional formula obtained by means of the revision of a formula with a new one. In particular, we focus on the size of the smallest formula equivalent to the revised knowledge base. The main result of this paper is that not all formalizations of belief revision are equal from this point of view. For some of them we show that the revised knowledge base can be expressed with a formula admitting a polynomialspace representation (we call these results "compactability" results). On the other hand we are able to prove that for other ones the revised knowledge base does not always admit a polynomialspace representation, unless the polynomial hierarchy collapses at a sufficiently low level ("noncompactability" results). The time complexity of query answering for the revised knowledge base has definitely an impact on being able to represent the result of the revision compactly. Nevertheless form...
Logical filtering
 In Proc. IJCAI03
, 2003
"... Filtering denotes any method whereby an agent updates its belief state—its knowledge of the state of the world—from a sequence of actions and observations. In logical filtering, the belief state is a logical formula describing possible world states and the agent has a (possibly nondeterministic) log ..."
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Cited by 46 (8 self)
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Filtering denotes any method whereby an agent updates its belief state—its knowledge of the state of the world—from a sequence of actions and observations. In logical filtering, the belief state is a logical formula describing possible world states and the agent has a (possibly nondeterministic) logical model of its environment and sensors. This paper presents efficient logical filtering algorithms that maintain a compact belief state representation indefinitely, for a broad range of environment classes including nondeterministic, partially observable STRIPS environments and environments in which actions permute the state space. Efficient filtering is also possible when the belief state is represented using prime implicates, or when it is approximated by a logically weaker formula. 1