Reasoning and querying bounds on differences with layered preferences

Artificial intelligence largely relies on bounds on differences (BoDs) to model binary constraints regarding different dimensions, such as time, space, costs, and calories. Recently, some approaches have extended the BoDs framework in a fuzzy, \u201cnoncrisp\u201d direction, considering probabilities or preferences. While previous approaches have mainly aimed at providing an optimal solution to the set of constraints, we propose an innovative class of approaches in which constraint propagation algorithms aim at identifying the \u201cspace of solutions\u201d (i.e., the minimal network) with their preferences, and query answering mechanisms are provided to explore the space of solutions as required, for example, in decision support tasks. Aiming at generality, we propose a class of approaches parametrized over user\u2010defined scales of qualitative preferences (e.g., Low, Medium, High, and Very High), utilizing the resume and extension operations to combine preferences, and considering different formalisms to associate preferences with BoDs. We consider both \u201cgeneral\u201d preferences and a form of layered preferences that we call \u201cpyramid\u201d preferences. The properties of the class of approaches are also analyzed. In particular, we show that, when the resume and extension operations are defined such that they constitute a closed semiring, a more efficient constraint propagation algorithm can be used. Finally, we provide a preliminary implementation of the constraint propagation algorithms

Context-Aware Rank-Oriented Recommender Systems

abstract: Recommender systems are a type of information filtering system that suggests items that may be of interest to a user. Most information retrieval systems have an overwhelmingly large number of entries. Most users would experience information overload if they were forced to explore the full set of results. The goal of recommender systems is to overcome this limitation by predicting how users will value certain items and returning the items that should be of the highest interest to the user. Most recommender systems collect explicit user feedback, such as a rating, and attempt to optimize their model to this rating value. However, there is potential for a system to collect implicit user feedback, such as user purchases and clicks, to learn user preferences. Additionally with implicit user feedback, it is possible for the system to remember the context of user feedback in terms of which other items a user was considering when making their decisions. When considering implicit user feedback, only a subset of all evaluation techniques can be used. Currently, sufficient evaluation techniques for evaluating implicit user feedback do not exist. In this thesis, I introduce a new model for recommendation that borrows the idea of opportunity cost from economics. There are two variations of the model, one considering context and one that does not. Additionally, I propose a new evaluation measure that works specifically for the case of implicit user feedback.Dissertation/ThesisM.S. Computer Science 201

Effective Team Strategies using Dynamic Scripting

Forming effective team strategies using heterogeneous agents to accomplish a task can be a challenging problem. The number of combinations of actions to look through can be enormous, and having an agent that is really good at a particular sub-task is no guarantee that agent will perform well on a team with members with different abilities. Dynamic Scripting has been shown to be an effective way of improving behaviours with adaptive game AI. We present an approach that modifies the scripting process to account for the other agents in a game. By analyzing an agent\u27s allies and opponents we can create better starting scripts for the agents to use. Creating better starting points for the Dynamic Scripting process and will minimize the number of iterations needed to learn effective strategies, creating a better overall gaming experience

Preference extraction and reasoning in negotiation dialogues

Modéliser les préférences des utilisateurs est incontournable dans de nombreux problèmes de la vie courante, que ce soit pour la prise de décision individuelle ou collective ou le raisonnement stratégique par exemple. Cependant, il n'est pas facile de travailler avec les préférences. Comme les agents ne connaissent pas complètement leurs préférences à l'avance, nous avons seulement deux moyens de les déterminer pour pouvoir raisonner ensuite : nous pouvons les inférer soit de ce que les agents disent, soit de leurs actions non-linguistiques. Plusieurs méthodes ont été proposées en Intelligence Artificielle pour apprendre les préférences à partir d'actions non-linguistiques mais à notre connaissance très peu de travaux ont étudié comment éliciter efficacement les préférences verbalisées par les utilisateurs grâce à des méthodes de Traitement Automatique des Langues (TAL).Dans ce travail, nous proposons une nouvelle approche pour extraire et raisonner sur les préférences exprimées dans des dialogues de négociation. Après avoir extrait les préférences de chaque tour de dialogue, nous utilisons la structure discursive pour suivre leur évolution au fur et à mesure de la conversation. Nous utilisons les CP-nets, un modèle de représentation des préférences, pour formaliser et raisonner sur ces préférences extraites. Cette méthode est d'abord évaluée sur différents corpus de négociation pour lesquels les résultats montrent que la méthode est prometteuse. Nous l'appliquons ensuite dans sa globalité avec des raisonnements issus de la Théorie des Jeux pour prédire les échanges effectués, ou non, dans le jeu de marchandage Les Colons de Catane. Les résultats obtenus montrent des prédictions significativement meilleures que celles de quatre baselines qui ne gèrent pas correctement le raisonnement stratégique. Cette thèse présente donc une nouvelle approche à la croisée de plusieurs domaines : le Traitement Automatique des Langues (pour l'extraction automatique des préférences et le raisonnement sur leur verbalisation), l'Intelligence Artificielle (pour la modélisation et le raisonnement sur les préférences extraites) et la Théorie des Jeux (pour la prédiction des actions stratégiques dans un jeu de marchandage)Modelling user preferences is crucial in many real-life problems, ranging from individual and collective decision-making to strategic interactions between agents for example. But handling preferences is not easy. Since agents don't come with their preferences transparently given in advance, we have only two means to determine what they are if we wish to exploit them in reasoning: we can infer them from what an agent says or from his nonlinguistic actions. Preference acquisition from nonlinguistic actions has been wildly studied within the Artificial Intelligence community. However, to our knowledge, there has been little work that has so far investigated how preferences can be efficiently elicited from users using Natural Language Processing (NLP) techniques. In this work, we propose a new approach to extract and reason on preferences expressed in negotiation dialogues. After having extracted the preferences expressed in each dialogue turn, we use the discursive structure to follow their evolution as the dialogue progresses. We use CP-nets, a model used for the representation of preferences, to formalize and reason about these extracted preferences. The method is first evaluated on different negotiation corpora for which we obtain promising results. We then apply the end-to-end method with principles from Game Theory to predict trades in the win-lose game The Settlers of Catan. Our method shows good results, beating baselines that don't adequately track or reason about preferences. This work thus presents a new approach at the intersection of several research domains: Natural Language Processing (for the automatic preference extraction and the reasoning on their verbalisation), Artificial Intelligence (for the modelling and reasoning on the extracted preferences) and Game Theory (for strategic action prediction in a bargaining game