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17
Propositional Independence: FormulaVariable Independence and Forgetting
 Journal of Artificial Intelligence Research
, 2003
"... Independence { the study of what is relevant to a given problem of reasoning { has received an increasing attention from the AI community. In this paper, we consider two basic forms of independence, namely, a syntactic one and a semantic one. We show features and drawbacks of them. In particular, ..."
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Cited by 55 (8 self)
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Independence { the study of what is relevant to a given problem of reasoning { has received an increasing attention from the AI community. In this paper, we consider two basic forms of independence, namely, a syntactic one and a semantic one. We show features and drawbacks of them. In particular, while the syntactic form of independence is computationally easy to check, there are cases in which things that intuitively are not relevant are not recognized as such. We also consider the problem of forgetting, i.e., distilling from a knowledge base only the part that is relevant to the set of queries constructed from a subset of the alphabet. While such process is computationally hard, it allows for a simpli  cation of subsequent reasoning, and can thus be viewed as a form of compilation: once the relevant part of a knowledge base has been extracted, all reasoning tasks to be performed can be simpli ed.
Integrity and Change in Modular Ontologies
, 2003
"... The benefits of modular representations are well known from many areas of computer science. In this paper, we concentrate on the benefits of modular ontologies with respect to local containment of terminological reasoning. We define an architecture for modular ontologies that supports local re ..."
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Cited by 41 (13 self)
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The benefits of modular representations are well known from many areas of computer science. In this paper, we concentrate on the benefits of modular ontologies with respect to local containment of terminological reasoning. We define an architecture for modular ontologies that supports local reasoning by compiling implied subsumption relations.
Factored planning
 In IJCAI’03
, 2003
"... We present a generalpurpose method for dynamically factoring a planning domain, whose structure is then exploited by our generic planning method to find sound and complete plans. The planning algorithm’s time complexity scales linearly with the size of the domain, and at worst exponentially with th ..."
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Cited by 38 (4 self)
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We present a generalpurpose method for dynamically factoring a planning domain, whose structure is then exploited by our generic planning method to find sound and complete plans. The planning algorithm’s time complexity scales linearly with the size of the domain, and at worst exponentially with the size of the largest subdomain and interaction between subdomains. The factorization procedure divides a planning domain into subdomains that are organized in a tree structure such that interaction between neighboring subdomains in the tree is minimized. The combined planning algorithm is sound and complete, and we demonstrate it on a representative planning domain. The algorithm appears to scale to very large problems regardless of the black box planner used. 1
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 32 (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.
Practical PartitionBased Theorem Proving for Large Knowledge Bases
, 2003
"... Query answering over commonsense knowledge bases typically employs a firstorder logic theorem prover. While firstorder inference is intractable in general, provers can often be handtuned to answer queries with reasonable performance in practice. ..."
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Cited by 26 (4 self)
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Query answering over commonsense knowledge bases typically employs a firstorder logic theorem prover. While firstorder inference is intractable in general, provers can often be handtuned to answer queries with reasonable performance in practice.
Querying Distributed Data through Distributed Ontologies: a Simple but Scalable Approach
"... In this paper, we define a simple but scalable framework for peertopeer data sharing systems, in which the problem of answering queries over a network of semantically related peers is always decidable. ..."
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Cited by 15 (4 self)
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In this paper, we define a simple but scalable framework for peertopeer data sharing systems, in which the problem of answering queries over a network of semantically related peers is always decidable.
First order LUB approximations: characterization and algorithms
 Artif. Intell
, 2005
"... One of the major approaches to approximation of logical theories is the upper and lower bounds approach introduced in (Selman and Kautz, 1991, 1996). In this paper, we address the problem of lowest upper bound (LUB) approximation in a general setting. We characterize LUB approximations for arbitrary ..."
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Cited by 14 (0 self)
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One of the major approaches to approximation of logical theories is the upper and lower bounds approach introduced in (Selman and Kautz, 1991, 1996). In this paper, we address the problem of lowest upper bound (LUB) approximation in a general setting. We characterize LUB approximations for arbitrary target languages, both propositional and first order, and describe algorithms of varying generality and efficiency for all target languages, proving their correctness. We also examine some aspects of the computational complexity of the algorithms, both propositional and first order; show that they can be used to characterize properties of whole families of resolution procedures; discuss the quality of approximations; and relate LUB approximations to other approaches existing in the literature which are not typically seen in the approximation framework, and which go beyond the “knowledge compilation ” perspective that led to the introduction of LUBs.
Modularization of Ontologies  WonderWeb: Ontology Infrastructure for the Semantic Web
, 2001
"... ..."
Approximation algorithms for treewidth
, 2002
"... Abstract. This paper presents algorithms whose input is an undirected graph, and whose output is a tree decomposition of width that approximates the optimal, the treewidth of that graph. The algorithms differ in their computation time and their approximation guarantees. The first algorithm works in ..."
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Cited by 6 (0 self)
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Abstract. This paper presents algorithms whose input is an undirected graph, and whose output is a tree decomposition of width that approximates the optimal, the treewidth of that graph. The algorithms differ in their computation time and their approximation guarantees. The first algorithm works in polynomialtime and finds a factorO(log OP T), where OP T is the treewidth of the graph. This is the first polynomialtime algorithm that approximates the optimal by a factor that does not depend on n, the number of nodes in the input graph. As a result, we get an algorithm for finding pathwidth within a factor of O(log OP T · log n) from the optimal. We also present algorithms that approximate the treewidth of a graph by constant factors of 3.66, 4, and 4.5, respectively and take time that is exponential in the treewidth. These are more efficient than previously known algorithms by an exponential factor, and are of practical interest. Finding triangulations of minimum treewidth for graphs is central to many problems in computer science. Realworld problems in artificial intelligence, VLSI design and databases are efficiently solvable if we have an efficient approximation algorithm for them. Many of those applications rely on weighted graphs. We extend our results to weighted graphs and weighted treewidth, showing similar approximation results for this more general notion. We report on experimental results confirming the effectiveness of our algorithms for large