Graph-based knowledge discovery has become a powerful tool in the machine learning and data mining areas. It provides a flexible and natural data representation to describe real world domains. In this research work we present a novel algorithm for graph-based approaches to deal with numerical attributes during the data processing phase implemented in the Subdue system. Our experimental results show that the use of numerical attributes increased classification accuracy in the Mutagenesis and PTC domains in 22% compared to the Subdue system when it does not use our numerical attributes handling approach. Our method also outperforms other author's results for the same domains, around 7% for the Mutagenesis domain and around 17% for the PTC domain.

We used a stimulus and response method in cognition to consider agents as situated in their specific (Binti Abdullah et al, 2010) to uncover correlation patterns context as it was realized that people are strongly affected of the physical artefact-communication during specific by, and possibly dependent on their environment contexts of communications. We found preliminary empirical (Susi & Ziemke, 2001). With this shift of focus, new interactive evidence that the physical artefacts influence the theories of cognition have emerged. These interactive communication process in a mutually constraining relationship theories such as situated cognition (Clancey, 1997), with the contexts. In which the context is made up and distributed cognition (Hutchins, 1999), are noted for of the teams' practice that includes how they collaborate, their emphasis on the relationship between cognition, and the physical setting, situations, and participation role.

List of organizers of the Twenty-First International Conference on Automated Planning and Scheduling, held in Freiburg, Germany, on June 11–16, 2011.

There are two primary types of graph-based data miners: frequent subgraph and compression-based miners. With frequent subgraph miners, the most interesting substructure is the largest one (or ones) that meet the minimum support. Whereas, compression-based graph miners discover those subgraphs that maximize the amount of compression that a particular substructure provides a graph. The algorithms associated with these two approaches are not only different, but they also may result in dramatic performance differences, as well as in the normative patterns being discovered. In order to compare these two types of graph-based approaches to knowledge discovery, in the following sections we will compare two publicly available applications: GASTON and SUBDUE.

Real-time heuristic search is a standard approach to pathfind- ing when agents are required to make decisions in a bounded, very short period of time. An assumption usually made in the development and evaluation of real-time algorithms is that the environment is unknown. Nevertheless, in many interesting applications such as pathfinding for automnomous characters in video games, the environment is known in advance. Recent real-time search algorithms such as D LRTA* and kNN LRTA* exploit knowledge about the environment while pathfinding under real-time constraints. Key to those algorithms is the computation of subgoals in a preprocessing step. Subgoals are subsequently used in the online planning phase to obtain high-quality solutions. Preprocessing in those algorithms, however, requires significant computation. In this paper we propose a novel preprocessing algorithm that generates subgoals using a series of backward search episodes carried out from potential goals. The result of a single backward search episode is a tree of subgoals that we then use while planning online. We show the advantages of our approach over state-of-the-art algorithms by carrying out experiments on standard real-time search benchmarks.

Linguistic and semantic annotations are important features for text-based applications. However, achieving and maintaining a good quality of a set of annotations is known to be a complex task. Many ad hoc approaches have been developed to produce various types of annotations, while comparing those annotations to improve their quality is still rare. In this paper, we propose a framework in which both linguistic and domain information can cooperate to reason with annotations. The underlying knowledge representation issues are carefully analyzed and solved by studying a higher order logic, which accounts for the cooperation of different sorts of knowledge. Our prototype implements this logic based on a reduction to classical description logics by preserving the semantics, allowing us to benefit from cutting-edge Semantic Web reasoners. An application scenario shows interesting merits of this framework on reasoning with annotations of texts.

Under the Defense Advanced Research Project Agency's (DARPA) Integrated Crisis Early Warning System (ICEWS), Innovative Decisions, Inc. (IDI) constructed a Bayesian network to combine forecasts produced by a set of social science models. We used Bayesian network structure learning with political science variables to produce meaningful priors. We employed a naive Bayes structure to aggregate the forecasts. In both cases, IDI improved classification by intelligently discretizing continuous variables. The resulting network not only met performance criteria set by DARPA, but also out-performed each of the social science models across all types of forecasted events. We describe the construction of the aggregator as well as a set of experiments performed to explore the nature of the Bayesian EOI Aggregator's performance.

Officers of  FLAIRS in 2011.

In a competitive game it is important to identify the opponent's strategy as quickly and accurately as possible so that an effective response can be planned. In this vein, this paper summarizes our work in exploring using first order inductive learning to learn rules for representing opponent strategies. Specifically, we use these learned rules to perform plan recognition and classify an opponent strategy as one of multiple learned strategies. Our experiments validate this novel approach in a simple real-time strategy game.

Natural language processing and statistical methods were used to identify linguistic features associated with the quality of student-generated paragraphs. Linguistic features were assessed using Coh-Metrix. The resulting computational models demonstrated small to medium effect sizes for predicting paragraph quality: introduction quality r2 = .25, body quality r2 = .10, and conclusion quality r2 = .11. Although the variance explained was somewhat low, the linguistic features identified were consistent with the rhetorical goals of paragraph types. Avenues for bolstering this approach by considering individual writing styles and techniques are considered.