In many data-centric semantic web applications, it is desirable to use OWL to encode the Integrity Constraints (IC) that must be satisfied by instance data. However, challenges arise due to the Open World Assumption (OWA) and the lack of a Unique Name Assumption (UNA) in OWL’s standard semantics. In particular, conditions that trigger constraint violations in systems using the ClosedWorld Assumption (CWA), will generate new inferences in standard OWL-based reasoning applications. In this paper, we present an alternative IC semantics for OWL that allows applications to work with the CWA and the weak UNA. Ontology modelers can choose which OWL axioms to be interpreted with our IC semantics. Thus application developers are able to combine open world reasoning with closed world constraint validation in a flexible way. We also show that IC validation can be reduced to query answering under certain conditions. Finally, we describe our prototype implementation based on the OWL reasoner Pellet.

As we see later, the mathematical derivations are more elaborate A Bayesian network is a probabilistic graphical model that than those recently introduced for BIC and AIC criteria relies on a structured dependency among random variables (de Campos, Zeng, and Ji 2009), and the reduction in the to represent a joint probability distribution in a compact and search space and cache size are less effective when priors efficient manner. It is composed by a directed acyclic graph are strong, but still relevant. This is expected, as the BIC (DAG) where nodes are associated to random variables and score is known to penalize complex graphs more than BD conditional probability distributions are defined for variables scores do. We show that the search space can be reduced given their parents in the graph. Learning the graph (or without losing the global optimality guarantee and that the structure) of these networks from data is one of the most memory requirements are small in many practical cases.

Many problems in AI, including planning, logical reasoning and probabilistic inference, have been shown to reduce to (weighted) constraint satisfaction. While there are a number of approaches for solving such problems, the recent gains in efficiency of the satisfiability approach have made SAT solvers a popular choice. Modern propositional SAT solvers are efficient for a wide variety of problems. However, particularly in the case of spatial reasoning, conversion to propositional SAT can sometimes result in a large number of variables and/or clauses. Moreover, spatial reasoning problems can often be more efficiently solved if the agent is able to exploit the geometric nature of space to make better choices during search and backtracking. The result of these two drawbacks — larger problem sizes and inefficient search — is that even simple spatial constraint problems are often intractable in the SAT approach. In this paper we propose a spatial reasoning system that provides significant performance improvements in constraint satisfaction problems involving spatial predicates. The key to our approach is to integrate a diagrammatic representation with a DPLL-based backtracking algorithm that is specialized for spatial reasoning. The resulting integrated system can be applied to larger and more complex problems than current approaches and can be adopted to improve performance in a variety of problems ranging from planning to probabilistic inference

The Association for the Advancement of Artificial Intelligence was pleased to present the 2009 Fall Symposium Series, held Thursday through Saturday, November 5–7, at the Westin Arlington Gateway in Arlington, Virginia. The Symposium Series was preceded on Wednesday, November 4 by a one-day AI funding seminar. The titles of the seven symposia were as follows: (1) Biologically Inspired Cognitive Architectures, (2) Cognitive and Metacognitive Educational Systems, (3) Complex Adaptive Systems and the Threshold Effect: Views from the Natural and Social Sciences, (4) Manifold Learning and Its Applications, (5) Multirepresentational Architectures for Human-Level Intelligence, (6) The Uses of Computational Argumentation, and (7) Virtual Healthcare Interaction.

Series, held Thursday through Saturday, November 5-7, at he Association for the Advancement of Artificial Intelligence the Westin Arlington Gateway in Arlington, Virginia. The titles of the seven symposia were as follows: (1) Biologically Inspired Cognitive Biologically Inspired Cognitive Architectures Architectures, (2) Cognitive and Metacognitive Cognitive and Metacognitive Educational Systems Educational Systems, (3) Complex Adaptive Complex Adaptive Systems and the Threshold Effect: Views from the Natural Systems and the Threshold Effect: Views and Social Sciences from the Natural and Social Sciences, (4) Manifold Manifold Learning and Its Applications Learning and Its Applications, (5) Multirepresentational Architectures for Human-Level Multirepresentational Architectures for Human-Level Intelligence Intelligence, (6) The Uses of Computational The Uses of Computational Argumentation Argumentation, and (7) Virtual Healthcare Virtual Healthcare Interaction Interaction. An informal reception was held on Thursday, November 5. A general plenary session, in which the highlights of each symposium were presented, was held on Friday, November 6. The challenge of creating a real-life computational equivalent of the human mind requires that we better understand at a computational level how natural intelligent systems develop their cognitive and learning functions. They will behave, variety of disjoined communities and schools of learn, communicate, and "think" as conscious thought that used to speak different languages and beings in general, in addition to being able to perform ignore each other.

The conference Robotics: Science and Systems was held at the University of Washington in Seattle, from June 28 to July 1, 2009. More than 300 international researchers attended this single‐track conference to learn about the most exciting robotics research and most advanced robotic systems. The program committee selected 39 papers out of 154 submissions. The program also included invited talks. The plenary presentations were complemented by workshops.

As the pace of Linked data generation and usage increases, so does the interest in intelligent, usable, and scalable browsing tools. Faceted browsing has potential to provide a foundation for effective dataset navigation. In this paper, we will discuss some of the anticipated benefits along with some associated challenges in building the next-generation faceted browsing system for the Web of Linked Data. We also present our initial system design and implementation.

There is a growing awareness and interest in the issues of accountability and transparency in the pursuit of digital privacy. In previous work, we asserted that systems needed to be “policy aware” and able to compute the likely compliance of any digital transaction with the associated privacy policies (law, rule, or contract). This paper focuses on one critical step in respecting privacy in a digital environment, that of understanding the context associated with each digital transaction. For any individual transaction, the pivotal fact may be context information about the data, the party seeking to use it, the specific action to be taken, or the associated rules. We believe that the granularity of semantic web representation is well suited to this challenge and we support this position in the paper.

Data.gov is a website that provides US Government data to the general public to ensure better accountability and transparency. Our recent work on the Data-gov Wiki, which attempts to integrate the datasets published at Data.gov into the Linking Open Data (LOD) cloud (yielding "linked government data"), has produced 5 billion triples – covering a range of topics including: government spending, environmental records, and statistics on the cost and usage of public services. In this paper, we investigate the role of Semantic Web technologies in converting, enhancing and using linked government data. In particular, we show how government data can be (i) inter-linked by sharing the same terms and URIs, (ii) linked to existing data sources ranging from the LOD cloud (e.g. DBpedia) to the conventional web (e.g. the New York Times), and (iii) cross-linked by their knowledge provenance (which captures, among other things, derivation and revision histories).

Funding for the conference was provided by the National Science Foundation, the Naval Research Laboratory, ABB, Microsoft Research, Microsoft Robotics, Evolution Robotics, Willow Garage, and Intel. Springer sponsored the best student paper award. The meeting brought together more than 280 researchers from Europe, Asia, North America, and Australia. He showed how molecular motors exploit for the technical program. Twenty of the accepted thermal noise to achieve energy efficiency and papers were presented orally; the remaining 20 talked about the implications for building artificial were presented as posters.