Program committee for the Twenty-First International Conference on Automated Planning and Scheduling, held in Freiburg, Germany, on June 11–16, 2011.

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

Defeasible argumentation and default reasoning are usually perceived as two similar, but distinct approaches to commonsense reasoning. In this paper, we combine these two fields by viewing (defeasible resp. default) rules as a common crucial part in both areas. We will make use of possible worlds semantics from default reasoning to provide examples for arguments, and carry over the notion of plausibility to the argumentative framework. Moreover, we base a priority relation between arguments on the tolerance partitioning of system Z and obtain a criterion phrased in system Z terms that ensures warrancy in defeasible argumentation.

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.

The filter model of feature selection has been well studied. In previous studies, classification performance has traditionally been proposed as a way to evaluate filter solutions. In this study, a new method of comparing feature ranking techniques is presented providing a straightforward approach for quantifying individual filters’ robustness to class noise. Six commonly-used filters, plus one which is rarely used, are investigated regarding their ability to retain, in the presence of class noise, strong classification performance. Three classifiers and one classification performance metric are considered. The experimental results of this study show that Gain Ratio, one of the well known and widely used filters, is very sensitive to class noise. ReliefF offers the best results with both the NB and kNN learners while Signal-to-noise, though not as widely used in the literature as the others, outperforms all the filters with the SVM learner.

If given manually-crafted goal selection knowledge, goal reasoning agents can dynamically determine which goals they should achieve in complex environments. These agents should instead learn goal selection knowledge through expert interaction. We describe T-ARTUE, a goal reasoning agent that performs case-based active and interactive learning to discover goal selection knowledge. We also report tests of its performance in a complex environment. We found that, under some conditions, T-ARTUE can quickly learn goal selection knowledge.

Negation is present in all human languages and it is used to reverse the polarity of parts of a statement. It is a complex phenomenon that interacts with many other aspects of language. Besides the direct meaning, negated statements often carry a latent positive meaning. Negation can be interpreted in terms of its scope and focus. This paper explores the importance of both scope and focus to capture the meaning of negated statements. Some issues on detecting negation from text are outlined, the forms in which negation occurs are depicted and heuristics to detect its scope and focus are proposed.

The aim of Belief Merging is to aggregate possibly conflicting pieces of information issued from different sources. The quality of the resulting set is usually considered in terms of a closeness criterion between the resulting belief set and the initial belief sets. The notion of distance between belief sets is thus a crucial issue when we face the merging problem. The aim of this paper is twofold: introducing a syntactical way to calculate distances and proposing the use of a distance based on prime implicants and prime implicates that considers the importance of each propositional symbol in the belief set.

List of the organizers and conference committees for FLAIRS-24, including special tracks.

We propose a new optimization algorithm for Multiple Kernel Learning (MKL) called SpicyMKL, which is applicable to general convex loss functions and general types of regularization. The proposed SpicyMKL iteratively solves smooth minimization problems. Thus, there is no need of solving SVM, LP, or QP internally. SpicyMKL can be viewed as a proximal minimization method and converges super-linearly. The cost of inner minimization is roughly proportional to the number of active kernels. Therefore, when we aim for a sparse kernel combination, our algorithm scales well against increasing number of kernels. Moreover, we give a general block-norm formulation of MKL that includes non-sparse regularizations, such as elastic-net and \ellp -norm regularizations. Extending SpicyMKL, we propose an efficient optimization method for the general regularization framework. Experimental results show that our algorithm is faster than existing methods especially when the number of kernels is large (> 1000).