We study the computational complexity of optimal bribery and manipulation schemes for sports tournaments with uncertain information: cup; challenge or caterpillar; and round robin. Our results carry over to the equivalent voting rules: sequential pair-wise elections, cup, and Copeland, when the set of candidates is exactly the set of voters. This restriction creates new difficulties for most existing algorithms. The complexity of bribery and manipulation are well studied, almost always assuming deterministic information about votes and results. We assume that for candidates i and j the probability that i beats j and the costs of lowering each probability by fixed increments are known to the manipulators. We provide complexity analyses for cup, challenge, and round robin competitions ranging from polynomial time to NP^PP. This shows that the introduction of uncertainty into the reasoning process drastically increases the complexity of bribery problems in some instances.

Children with cerebral palsy have difficulty moving their hands and muscles due to developmental issues. One way to assist these children is by having them participate in physical therapy. The best form of physical therapy for children is playing. Playing is a natural activity for children, and it also helps in furthering the developments of muscles. This form of therapy is perhaps a greater choice for children because it keeps the child engaged due to the interest the child holds in the activity. By integrating two projects done by previous students, a Pleo that is controlled by a Wii Nunchuk will be able to teach Pleo how to dance. The child will be engaged in this activity for long durations because there are many variations of dance that the Pleo can learn by moving many body parts. Children using this toy will have continuous movement in their arm muscles by moving the Nunchuk for the duration of the activity. This toy will not only help children with severe disabilities feeling equal to their non-disabled peers by allowing them to use controllers found on many game consoles, but it will also enhance the child’s self-esteem and confidence by allowing them to control the outcome of the Pleo.

This paper compared the linguistic and psychological word uses in English and Chinese languages with LIWC (Linguistic Inquiry and Word Count) programs. A Principal Component Analysis uncovered six linguistic and psychological components, among which five components were significantly correlated. The correlated components were ranked as Negative Valence (r=.92), Embodiment (r=.88), Narrative (r=.68), Achievement (r=.65), and Social Relation (r=.64). However, the results showed the order of the representative features differs in two languages and certain word categories co-occurred with different components in English and Chinese. The differences were interpreted from the perspective of distinctive eastern and western cultures.

For many classification problems, genetic algorithms prove to be effective without extensive domain engineering. However, the chess King-Rook-King endgame problem appears to be an exception. We explore whether modifications to a baseline parallel genetic algorithm can improve the accuracy on this particular problem. After describing the problem domain and our implementation of a parallel genetic algorithm, we present an empirical evaluation of several approaches intended to improve overall performance. Our results confirm the challenging nature of this domain. We describe several directions that may yet deliver significant improvements.

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.

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.

Real-time Strategy (RTS) games are complex domains which are a significant challenge to both human and artificial intelligence (AI). For that reason, and although many AI approaches have been proposed for the RTS game AI problem, the AI of all commercial RTS games is scripted and offers a very static behavior subject to exploits. In this paper, we will focus on a case-based reasoning (CBR) approach to this problem, and concentrate on the process of case-acquisition. Specifically, we will describe 7 different techniques to automatically acquire plans by observing human demonstrations and compare their performance when using them in the Darmok 2 system in the context of an RTS game.