Conversational case-based reasoning systems use an interactive dialog to retrieve stored cases. Normally the ordering of questions in this dialog is chosen based only on their discriminativeness. However, because the user may not be able to answer all questions, even highly discriminative questions are not guaranteed to provide information. This paper presents a customization method CCBR systems can apply to adjust entropy-based discriminativeness considerations by predictions of user ability to answer questions. The method uses a naive Bayesian classifier to classify users into user groups based on the questions they answer, applies information from group profiles to predict which future questions they are likely to be able to answer, and selects the next questions to ask based on a combination of information gain and response likelihood. The method was evaluated for a mix of simulated user groups, each associated with particular probabilities for answering questions about each case indexing feature, in four sample domains. For simulated users with varying abilities to answer particular questions, results showed improvement in dialog length over a non-customized entropy-based approach in all test domains.

Mobile robots are becoming more relevant and an essential part of our everyday lives. They are increasingly taking their place in service-oriented applications including domestic and entertainment roles. They are beginning to open up many potential opportunities, but they still come with challenges in terms of their limited sensing capability and accuracy. In this project, we addressed these fundamental problems with mobile robotics and demonstrate our approach to each of the problems with a mobile robot equipped with low-cost and low-end devices. The r-one swarm robot is a low-cost multi-robot systems platform that is advanced enough for multi-robot research, robust enough for undergraduate and graduate education and cheap enough for K-12 outreach. As robots become more and more useful, multiple robots working together on a single task will become commonplace. Many of the most useful applications of robots are particularly well-suited to this “swarm” approach. Groups of robots can perform these tasks more efficiently, and can perform them in fundamentally different ways than robots working individually. However, swarms of robots are difficult to program and coordinate.

Over the past 11 years, this FLAIRS special track program has provided a focal point for the North American case-based reasoning (CBR) community, though it has drawn good international participation as well. Five papers were accepted this year. Ontañón presents seven different case acquisition techniques for CBR systems that use learning from demonstration and performs a comparative evaluation in the context of real-time strategy games. Ontañón and Plaza describe a preliminary formal model of knowledge transfer in case-based inference based on the idea of partial unification. Jalali and Leake present a new approach for ordering questions in conversational CBR systems that takes into account not just their discriminativeness but also the user's ability to answer.

In the Silver Anniversary year of FLAIRS, in an effort to promote discussion of emerging ideas and work in order to encourage and help guide researchers, especially new researchers, the program committee added the poster abstract submission category. This allows researchers to present a full poster in the conference poster session and receive that critical, work-shaping feedback that helps guide good work into great work.

Animal behavior analysis has, in the past, taken a very low tech approach, with direct observer surveillance and automated video surveillance as the norm. These methods are insufficient when one wants to study interactions between large numbers of animals in their housing environment. In this paper we use a housing environment that has been equipped with a system of RFID sensors. RFID transponders were implanted into the study animal, the naked mole rat. The resulting data was analyzed using principal component analysis and frequent pattern mining. Results showed that these methods can identify time periods of high behavioral activity from that of low activity, along with which groups of animals interacted with one another

Propositional attitudes in noncompositional logic are analysed from the view point of integration of their epistemic and deontic components. A new logical calculus for propositional attitudes inspired by possibility theory, a noncompositional version of fuzzy logic is proposed.

Associations between people and other concepts are common in text and range from distant to close connections. This paper discusses and justifies the need to consider subtypes of the generic relation ASSOCIATION. Semantic primitives are used as a concise and formal way of specifying the key semantic differences between subtypes. A taxonomy of association relations is proposed, and a method based on composing previously extracted relations is used to extract subtypes. Experimental results show high precision and moderate recall.

This work presents an algorithm to provide a better represen- tation of space to artificially intelligent characters (i.e., agents or bots) in game and simulation environments by providing a more accurate breakdown of the traversable space present in the game environment. Such representations are generally constructed by decomposing the walkable space present in a game environment into a series of convex regions to form a data structure called a navigation mesh. We extend the basic concept of a navigation mesh by the introduction of an understanding of the textures that are attached to the underlying geometry creating what we refer to as a texture-aware navigation mesh. This does result in a more complex navigation mesh (more regions and a larger search space). However, since the textures of walkable geometry can be used to determine the appropriate traversal method for that terrain, a game character can determine valid paths for their traversal methods using just the navigation mesh (e.g., characters in cars can generate paths containing just roads or walking characters can create paths containing just sidewalks). We also present a use case that shows how such a system of texture aware naviga- tion meshes might benefit character path planning and search in virtual environments. In this use case, we examine a Real Time Strategy game style game environment, which shows it is possible to generate a navigation mesh such that each region is composed of a single terrain type.

Psychosocial assessments and treatments are effective for a range of psychological problems.One particular area of concern is youth suicide. This paper reports on the SAMHT intelligent tutoring system, which provides youth suicide risk assessment training.SAMHT's interactive avatar interface is based on an intelligent backend, and provides a believable interaction that is effective for training mental health professionals.

Household appliances such as dishwashers, televisions and radios are an indispensable part of the modern household. Yet, people who have some form of physical impairment often find that they are unable to make use of these commonly available appliances, to the detriment of their lifestyle. This paper proposes a gesture interface for home appliances that can be used by people with physical impairments. Two simulated gesture controlled appliances are developed and evaluated by physically impaired people. The results show that this interface is able to allow physically impaired people to make use of modern appliances by gesture.