Showing papers in "Artificial Intelligence in 2010"

TL;DR: An overview of HSMMs is presented, including modelling, inference, estimation, implementation and applications, which has been applied in thirty scientific and engineering areas, including speech recognition/synthesis, human activity recognition/prediction, handwriting recognition, functional MRI brain mapping, and network anomaly detection.

TL;DR: A theoretic framework based on rough set theory, called positive approximation, is introduced, which can be used to accelerate a heuristic process of attribute reduction, and several representative heuristic attribute reduction algorithms inrough set theory have been enhanced.

TL;DR: The results of the study show that, compared to manufacturers' defaults, interfaces automatically generated by Supple significantly improve speed, accuracy and satisfaction of people with motor impairments.

TL;DR: It is shown how the growing Semantic Web provides necessary support for these technologies, the challenges in bringing the technology to the next level, and some starting places for the research are proposed.

TL;DR: The semantics of natural language expressions concerning space in this way offers a substantial simplification of the general problem of relating natural spatial language to its contextualized interpretation.

TL;DR: A new mixed-integer linear program (MILP) for Stackelberg games to consider human adversaries is introduced, incorporating: (i) novel anchoring theories on human perception of probability distributions and (ii) robustness approaches for MILPs to address human imprecision.

TL;DR: This paper introduces many new ideas that develop early ''compression tests'' and the more recent definition of '' universal intelligence'' in order to design new ''universal intelligence tests'', one of which is the ''anytime intelligence test'', which adapts to the examinee's level of intelligence within a limited time.

TL;DR: The resulting logic, designed specifically to cater for responsibility and delegation, can be employed to offer an axiological and semantic exploration of the related concepts of forbearance, imperatives and group communication.

TL;DR: This paper extends the notion of arc consistency by allowing fractional weights and by allowing several arc consistency operations to be applied simultaneously and shows that an optimal arc consistency closure can theoretically be determined in polynomial time by reduction to linear programming.

TL;DR: The idea is to use existing inconsistency measures in order to define a game in coalitional form, and then to use the Shapley value to obtain an inconsistency measure that indicates the responsibility/contribution of each formula to the overall inconsistency in the base.

TL;DR: In this article, a planning framework for the automated composition of Web services is presented, which can handle services specified and implemented using industrial standard languages for business processes modeling and execution, like ws-bpel.

TL;DR: A new filtering algorithm based on local all different constraints is introduced that is stronger than other existing filterings and also more efficient - i.e., it allows one to solve more instances quicker.

TL;DR: In this article, a semantic lens is proposed to enhance the potential of the semantic structure and help individuals build and retrieve semantic images from different facets, abstraction levels and scales through time.

TL;DR: It is demonstrated that different types of minimal modules induced by inseparability relations can be automatically extracted from real-world medium-size DL-Lite ontologies by composing the known tractable syntactic locality-based module extraction algorithm with the authors' non-tractable extraction algorithms and using the multi-engine QBF solver aqme.

TL;DR: This article presents a novel algorithm called LAMP (Learning Action Models from Plan traces), to learn action models with quantifiers and logical implications from a set of observed plan traces with only partially observed intermediate state information.

TL;DR: In this article, the notion of treewidth has been applied to logic-based reasoning problems such as abduction, closed world reasoning, circumscription, and disjunctive logic programming.

TL;DR: A way of avoiding this difficulty using several rounds of instance selection on subsets of the original dataset using a voting scheme to allow good performance in terms of testing error and storage reduction, while the execution time of the process is significantly reduced.

TL;DR: The broken-triangle property is introduced, which allows us to define a novel tractable class for this problem which significantly generalizes the class of problems with tree structure and can be detected in polynomial time.

TL;DR: The author hopes that this paper will be a useful map for AI researchers who are going to explore further and deeper connections between AI and quantum computation as well as quantum theory although some parts of the map are very rough and other parts are empty, and waiting for the readers to fill in.

TL;DR: The aim of the research presented in this paper is to forecast air traffic controller workload and required airspace configuration changes with enough lead time and with a good degree of realism, using tree search methods combined with a neural network.

TL;DR: This paper identifies a range of social attributes in an open-network setting that influence people's decision-making and thus affect the performance of computer-agent strategies, and establishes the importance of learning and adaptation to the success of such strategies.

TL;DR: It is shown that several other notions of an answer set proposed in the literature for programs with constraints can be obtained within this framework as the results of appropriately selected classes of computations.

TL;DR: This paper describes and studies three MCTS-based methods for playing the wargame-like board game, Kriegspiel, and compares them to the strongest known minimax-based KriegSpiel program, obtaining significantly better experimental results with less domain-specific knowledge.

TL;DR: A fairly general framework for reasoning from inconsistent propositional bases is defined, which encompasses several previous approaches as specific cases, including reasoning from preferred consistent subsets, and some forms of information merging.

TL;DR: This paper provides a cubic algorithm for checking the consistency of complete networks of basic CDC constraints, and proves that reasoning with the CDC is in general an NP-complete problem.

TL;DR: This model shows that acyclic CP-nets are not learnable with equivalence queries alone, even if the examples are restricted to swaps for which dominance testing takes linear time, and reveals that active queries are required for efficiently learningCP-nets in large multi-attribute domains.

TL;DR: It is demonstrated that, as with Causal Bayesian Networks (CBNs), the identifiability of the effects of causal manipulations when observations of the system are incomplete can be verified simply by reference to the topology of the CEG.

TL;DR: This work defines a novel hierarchical POMDP-based approach for visual processing management and shows empirically that HiPPo and CP outperform the naive application of all visual operators on all ROIs and produces more robust plans than CP or the naive visual processing.

TL;DR: This work describes a procedure that takes a deterministic planning problem, a forward-search state, and a set of abstraction-based admissible heuristics, and derives an optimal additive composition of these heuristic with respect to the given state.

TL;DR: This work proposes here an extension of the Faber-Leone-Pfeifer semantics, or FLP semantics, to the full propositional language, which reveals both common threads and differences between the FLP and stable-model semantics.