Inductive Logic Programming (ILP) deals with the problem of finding a hypothesis covering positive examples and excluding negative examples, where both hypotheses and examples are expressed in first-order logic. In this paper we employ constraint satisfaction techniques to model and solve a problem known as template ILP consistency, which assumes that the structure of a hypothesis is known and the task is to find a unification of the contained variables such that no negative example is subsumed by the hypothesis and all positive examples are subsumed.

Approximation (WARA-2010), scheduled to be held on July Topics of interest for this AAAI workshop include all 12, 2010 in Atlanta, Georgia, USA in conjunction with aspects of abstraction, reformulation and approximation, AAAI-10, aims to provide a forum for intensive interaction including (but not limited to) the following: new techniques among researchers in all areas of artificial intelligence for automatically constructing and selecting appropriate and computer science with an interest in the different aspects ARA methods; frameworks that unify and classify of abstraction, reformulation, and approximation techniques. ARA techniques; empirical and theoretical studies of the The goal and scope of this workshop are similar to costs and benefits of ARA; applications of ARA to search, an independent symposium called SARA. The diverse backgrounds constraint satisfaction, deterministic and probabilistic planning, of participants of previous SARA symposia has led theorem proving, logic programming, game playing, to a rich and lively exchange of ideas, allowed the comparison parallel and distributed search, distributed data and knowledge of goals, techniques, and paradigms, and helped identify bases, internet search and navigation, knowledge compilation, important research issues and engineering hurdles. This knowledge acquisition, knowledge reformulation, workshop continues to do the same.

The starting point is an ongoing effort to structure underlying intelligent behavior, whether intended reconstruct cognitive architectures from the ground up via as models of human intelligence and/or implementations of graphical models (Koller and Friedman 2009), with the artificial intelligence (Langley, Laird and Rogers 2009). A aim of understanding existing architectures better, basic cognitive architecture may comprise memories, exploring the overall space of architectures, and decision algorithms, learning mechanisms, and some developing new and improved architectures (Rosenbloom means of interacting with external environments.

Reasoning by the usage of mental images has been the subject of much debate in Cognitive Science, especially among the schools of depictive and descriptive imagistic representations. Whether or not reasoning with mental images involves a mechanism or a process different from language based reasoning is an important question. This paper proposes that any theory which aims for a cohesive whole needs to be constrained by neurophysiological data and such data can be obtained by the Clock Drawing Test. The Clock Drawing Test (CDT) is a screening tool for cognitive impairment and can be used as a tool to test resilience of certain factors of visual spatial representations. Thus, it can help to form an empirical case for which factors are prone to debility and which factors are not during the onset and progress of cognitive impairment from a mental representation point of view. This paper presents 50 CDT tests done on patients with cognitive impairment and analyses the results which support the case for a depictive rather than a descriptive theory for imagistic representations. Lastly, this paper proposes that there is some evidence for a more dynamic and distributed nature of representation in the observations which question the above dichotomy and can be partly explained by certain aspects of the connectionist school of thought.

The architectural approach to AI focuses on the fixed structure underlying intelligence. Applying it to statistical relational AI should further stimulate the application of statistical relational techniques across AI, while focusing research on their commonalities, (in)compatibilities and integration. It could also yield new architectures that are simpler yet more comprehensive than today’s best.

Machine reading aims to automatically extract knowledge from text. It is a long-standing goal of AI and holds the promise of revolutionizing Web search and other fields. In this paper, we analyze the core challenges of machine reading and show that statistical relational AI is particularly well suited to address these challenges. We then propose a unifying approach to machine reading in which statistical relational AI plays a central role. Finally, we demonstrate the promise of this approach by presenting OntoUSP, an end-to-end machine reading system that builds on recent advances in statistical relational AI and greatly outperforms state-of-the-art systems in a task of extracting knowledge from biomedical abstracts and answering questions.

A new method is proposed for compiling causal independencies into Markov logic networks. A Markov logic network can be viewed as compactly representing a factorization of a joint probability into the multiplication of a set of factors guided by logical formulas. We present a notion of causal independence that enables one to further factorize the factors into a combination of even smaller factors and consequently obtain a finer-grain factorization of the joint probability. The causal independence lets us specify the factor in terms of weighted, directed clauses and an associative and commutative operator, such as "or", "sum" or "max", on the contribution of the variables involved in the factors, hence combining both undirected and directed knowledge.

Learning the relational structure of a domain is a fundamental problem in statistical relational learning. The deep transfer algorithm of Davis and Domingos attempts to improve structure learning in Markov logic networks by harnessing the power of transfer learning, using the second-order structural regularities of a source domain to bias the structure search process in a target domain. We propose that the clique-scoring process which discovers these second-order regularities constitutes a novel standalone method for learning the structure of Markov logic networks, and that this fact, rather than the transfer of structural knowledge across domains, accounts for much of the performance benefit observed via the deep transfer process. This claim is supported by experiments in which we find that clique scoring within a single domain often produces results equaling or surpassing the performance of deep transfer incorporating external knowledge, and also by explicit algorithmic similarities between deep transfer and other structure learning techniques.

Most of the existing weight-learning algorithms for Markov Logic Networks (MLNs) use batch training which becomes computationally expensive and even infeasible for very large datasets since the training examples may not fit in main memory. To overcome this problem, previous work has used online learning algorithms to learn weights for MLNs. However, this prior work has only applied existing online algorithms, and there is no comprehensive study of online weight learning for MLNs. In this paper, we derive new online algorithms for structured prediction using the primal-dual framework, apply them to learn weights for MLNs, and compare against existing online algorithms on two large, real-world datasets. The experimental results show that the new algorithms achieve better accuracy than existing methods.

Previous work on the YAPPR plan recognition system provided algorithms for translating conventional HTN plan libraries into lexicalized grammars and treated the problem of plan recognition as one of parsing. To produce these grammars required a fixed bound for any loops within the grammar and a presented a problem for optional actions within HTN plans. In this work we show that well known transformations from formal language theory can be used to rewrite the plan grammars to remove these limitations on the plan libraries.