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33
Probabilistic Logic Learning
 ACMSIGKDD Explorations: Special issue on MultiRelational Data Mining
, 2004
"... The past few years have witnessed an significant interest in probabilistic logic learning, i.e. in research lying at the intersection of probabilistic reasoning, logical representations, and machine learning. A rich variety of di#erent formalisms and learning techniques have been developed. This pap ..."
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Cited by 43 (9 self)
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The past few years have witnessed an significant interest in probabilistic logic learning, i.e. in research lying at the intersection of probabilistic reasoning, logical representations, and machine learning. A rich variety of di#erent formalisms and learning techniques have been developed. This paper provides an introductory survey and overview of the stateof theart in probabilistic logic learning through the identification of a number of important probabilistic, logical and learning concepts.
Locationbased reasoning about complex multiagent behavior
 In Journal of Artificial Intelligence Research. AI Access Foundation
, 2011
"... Recent research has shown that surprisingly rich models of human activity can be learned from GPS (positional) data. However, most effort to date has concentrated on modeling single individuals or statistical properties of groups of people. Moreover, prior work focused solely on modeling actual succ ..."
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Cited by 11 (3 self)
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Recent research has shown that surprisingly rich models of human activity can be learned from GPS (positional) data. However, most effort to date has concentrated on modeling single individuals or statistical properties of groups of people. Moreover, prior work focused solely on modeling actual successful executions (and not failed or attempted executions) of the activities of interest. We, in contrast, take on the task of understanding human interactions, attempted interactions, and intentions from noisy sensor data in a fully relational multiagent setting. We use a realworld game of capture the flag to illustrate our approach in a welldefined domain that involves many distinct cooperative and competitive joint activities. We model the domain using Markov logic, a statisticalrelational language, and learn a theory that jointly denoises the data and infers occurrences of highlevel activities, such as a player capturing an enemy. Our unified model combines constraints imposed by the geometry of the game area, the motion model of the players, and by the rules and dynamics of the game in a probabilistically and logically sound fashion. We show that while it may be impossible to directly detect a multiagent activity due to sensor noise or malfunction, the occurrence of the activity can still be inferred by considering both its impact on the
Abduction and Learning
, 1996
"... . In this paper we study the problem of of integrating abduction and learning as they appear in Artificial Intelligence. A general comparison of abduction and induction as separate inferences and fields in AI is given. Based on this analysis we study their possible interaction and integration. We in ..."
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Cited by 8 (2 self)
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. In this paper we study the problem of of integrating abduction and learning as they appear in Artificial Intelligence. A general comparison of abduction and induction as separate inferences and fields in AI is given. Based on this analysis we study their possible interaction and integration. We introduce the notions of of abductive concept learning as a framework for learning with incomplete background theories. Together with this we propose a general methodology for incorporating abduction in inductive concept learning that allows us to exploit more fully in the learning process the knowledge contained in the background theory. This basic methodology extends in a natural way to the more general context of theory revision.
On Theory Revision with Queries
 In Proc. 12th Annu. Conf. on Comput. Learning Theory
, 1999
"... The theory revision, or concept revision, problem is to correct a given, roughly correct concept. Given the representation of an initial concept, one would like to obtain a representation of the target concept by applying revisions, that is, syntactic modifications such as the deletion of a var ..."
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Cited by 7 (5 self)
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The theory revision, or concept revision, problem is to correct a given, roughly correct concept. Given the representation of an initial concept, one would like to obtain a representation of the target concept by applying revisions, that is, syntactic modifications such as the deletion of a variable or a term. We give efficient revision algorithms using membership and equivalence queries for 2term monotone DNF, monotone kDNF, and readonce formulas. An example is given showing that some monotone DNF formulas cannot be revised efficiently. These results all assume that the revisions allowed are the replacements of a variable occurrence with a constant, which, for DNFs, corresponds to deletions of variables and terms. We also discuss a more general error model where besides deletions, additions are also allowed. 1 INTRODUCTION What the computational learning theory community calls a concept is often referred to as a theory elsewhere in artificial intelligence and logic....
More Efficient Windowing
, 1997
"... Windowing has been proposed as a procedure for efficient memory use in the ID3 decision tree learning algorithm. However, previous work has shown that windowing may often lead to a decrease in performance. In this work, we try to argue that rule learning algorithms are more appropriate for windowing ..."
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Cited by 5 (2 self)
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Windowing has been proposed as a procedure for efficient memory use in the ID3 decision tree learning algorithm. However, previous work has shown that windowing may often lead to a decrease in performance. In this work, we try to argue that rule learning algorithms are more appropriate for windowing than decision tree algorithms, because the former typically learn and evaluate rules independently and are thus less susceptible to changes in class distributions. Most importantly, we present a new windowing algorithm that achieves additional gains in efficiency by saving promising rules and removing examples covered by these rules from the learning window. While the presented algorithm is only suitable for redundant, noisefree data sets, we will also briefly discuss the problem of noisy data for windowing algorithms. Introduction Windowing is a general technique that aims at improving the efficiency of inductive classification learners by identifying an appropriate subset of the given t...
Theory revision with queries: Horn, readonce, and parity formulas
 Artificial Intelligence
, 2004
"... A theory, in this context, is a Boolean formula; it is used to classify instances, or truth assignments. Theories can model realworld phenomena, and can do so more or less correctly. The theory revision, or concept revision, problem is to correct a given, roughly correct concept. This problem is co ..."
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Cited by 5 (1 self)
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A theory, in this context, is a Boolean formula; it is used to classify instances, or truth assignments. Theories can model realworld phenomena, and can do so more or less correctly. The theory revision, or concept revision, problem is to correct a given, roughly correct concept. This problem is considered here in the model of learning with equivalence and membership queries. A revision algorithm is considered efficient if the number of queries it makes is polynomial in the revision distance between the initial theory and the target theory, and polylogarithmic in the number of variables and the size of the initial theory. The revision distance is the minimal number of syntactic revision operations, such as the deletion or addition of literals, needed to obtain the target theory from the initial theory. Efficient
Projective DNF formulae and their revision
 In Learning Theory and Kernel Machines, 16th Annual Conference on Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003
"... Valiant argued that biology imposes various constraints on learnability, and, motivated by these constraints, introduced his model of projection learning [14]. Projection learning aims to learn a target concept over some large domain, in this paper {0, 1} n, by learning some of its projections to a ..."
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Cited by 3 (1 self)
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Valiant argued that biology imposes various constraints on learnability, and, motivated by these constraints, introduced his model of projection learning [14]. Projection learning aims to learn a target concept over some large domain, in this paper {0, 1} n, by learning some of its projections to a class of smaller domains,
New revision algorithms
 In Algorithmic Learning Theory, 15th International Conference, ALT 2004
"... Abstract. A revision algorithm is a learning algorithm that identifies the target concept, starting from an initial concept. Such an algorithm is considered efficient if its complexity (in terms of the resource one is interested in) is polynomial in the syntactic distance between the initial and the ..."
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Cited by 2 (0 self)
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Abstract. A revision algorithm is a learning algorithm that identifies the target concept, starting from an initial concept. Such an algorithm is considered efficient if its complexity (in terms of the resource one is interested in) is polynomial in the syntactic distance between the initial and the target concept, but only polylogarithmic in the number of variables in the universe. We give efficient revision algorithms in the model of learning with equivalence and membership queries. The algorithms work in a general revision model where both deletion and addition type revision operators are allowed. In this model one of the main open problems is the efficient revision of Horn sentences. Two revision algorithms are presented for special cases of this problem: for depth1 acyclic Horn sentences, and for definite Horn sentences with unique heads. We also present an efficient revision algorithm for threshold functions. 1
Modeling Success, Failure, and Intent of MultiAgent Activities Under Severe Noise
"... Our society is founded on the interplay of human relationships and interactions. Since every person is tightly embedded in our social structure, the vast majority of human behavior can be fully understood only in the context of the actions of others. Thus, not surprisingly, more and more evidence is ..."
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Cited by 2 (2 self)
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Our society is founded on the interplay of human relationships and interactions. Since every person is tightly embedded in our social structure, the vast majority of human behavior can be fully understood only in the context of the actions of others. Thus, not surprisingly, more and more evidence is emerging from social networks
A Rational and Efficient Algorithm for View Revision
 in Databases. Applied Mathematics & Information Sciences 7(3
, 2013
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