This paper presents a case study in combining Lego Mindstorms NXT with Matlab/Simulink to help students in an undergraduate Machine Learning course study genetic algorithm design and testing. The project uses the VU-LRT toolbox to enable students to access the hardware capabilities of the Mindstorms platform from within Matlab. The course's enrollment was comprised of students from several majors with a variety of programming backgrounds. The course is part of an interdisciplinary cognitive science concentration. We report on the VU-LRT toolbox, the considerations imposed by the diversity of the student population on the design of the laboratory module and student evaluations of the laboratory module.

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.

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

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.

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.

One-on-one tutoring is significantly more effective than traditional classroom instruction. In recent years, automated tutoring systems are approaching that level of effectiveness by engaging students in rich natural language dialogue that contributes to learning. A promising approach for further improving the effectiveness of tutorial dialogue systems is to model the differential effectiveness of tutorial strategies, identifying which dialogue moves or combinations of dialogue moves are associated with learning. It is also important to model the ways in which experienced tutors adapt to learner characteristics. This paper takes a corpus- based approach to these modeling tasks, presenting the results of a study in which task-oriented, textual tutorial dialogue was collected from remote one-on-one human tutoring sessions. The data reveal patterns of dialogue moves that are correlated with learning, and can directly inform the design of student-adaptive tutorial dialogue management systems.

The quality criteria of system P have been guiding qualitative uncertain reasoning now for more than two decades. Different semantical approaches have been presented to provide semantics for system P. The aim of the present paper is to investigate the semantical structures underlying system P in more detail, namely, on the level of the models. In particular, we focus on the approach via conditional objects which relies on Boolean intervals, without making any use of qualitative or quantitative information. Indeed, our studies confirm the singular position of conditional objects, but we are also able to establish semantical relationships via novel variants of model theories.

In this paper, we present a programming paradigm for AI problem solving based on computational concepts drawn from Affective Computing. It is believed that emotions participate in human adaptability and reactivity, in behaviour selection and in complex and dynamic environments. We propose to define a mechanism inspired from this observation for general AI problem solving. To this purpose, we synthesize emotions as programming abstractions that represent the perception of the environment's state w.r.t. predefined heuristics such as goal distance, action capability,etc. We first describe the general architecture of this "emotion-oriented" programming model. We define the vocabulary that allows programmers to describe the problem to be solved (i.e. the environment), and the action selection function based on emotion abstractions (i.e. the agent's behaviours). We then present the runtime algorithm that builds emotions out of the environment, stores them in the agent's memory, and selects behaviours accordingly. We present the implementation of a classical labyrinth problem solver in this model. We show that the solutions obtained by this easy-to-implement emotion-oriented program are of good quality while having a reduced computational cost.

Social influence has no small effect on the preferences and behavior of agents in a social space. Contrary to rationality, we sometimes compromise our own needs for those of others. Thus, social influence has important implications in agent cognitive modeling for multi-objective decision-making problems. Namely, where these activities occur within a social context, the intentional preferences or utility of an agent may be subsumed, to a greater or lesser degree, by the influences of other agents. In this paper, a socially-aware model predictive controller is proposed using a social influence network theory and applied to a HVAC control problem. It transforms individual agent utility to socially-influenced utility reflecting interagent influences due to their existing relationships.

Several authors analyzed propositional attitudes ( wish , fear , regret , glad ) by integrating their epistemic and deontic components. This paper extends previous work done by the author and presents a logical calculus inspired by Possibility Theory, a non-compositional version of fuzzy logic.