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

Analyzing chat traffic has important applications for both the military and the civilian world. This poster will report on an effort to automatically separate chat messages into topic threads.

The paper proposes a method that uses topological information to guide path planning in any 2D workspace. Our method builds a topological environment based on the workspace to compute homotopy classes, which topologically describe how paths go through the obstacles in the workspace. Then, the homotopy classes are sorted according to an heuristic estimation of their lower bound. Only those with smaller lower bound are used to guide a planner based on the Rapidly-exploring Random Tree (RRT), called Homotopic RRT (HRRT), to compute the path in the workspace. Simulated and real results with an Autonomous Underwater V ehicle (AUV) are presented showing the feasibility of the proposal. Comparison with well-known path planning algorithms has also been included.

In this paper, we describe a framework dedicated to studies and experimentations upon the nature of the relationships between the rational reasoning process of an artificial agent and its psychological counterpart, namely its behavioral reasoning process. This study is focused on the domain of Conversational Assistant Agents, which are software tools providing various kinds of assistance to people of the general public interacting with computer-based applications or services. In this context, we show on some examples the need for the agents to be able to exhibit both a rational reasoning about the system functioning and a human-like believable dialogical interaction with the users.

With an explosion in the proliferation of user-generated content in communities, information overload is increasing and quality of readily available online content is deteriorating. There is an increasing need for intelligent systems that make use of implicit user generated knowledge in communities for community engagement. We describe our approach based on modeling user utterances in communities to proactively target the community for exchange of questions and answers. We envision a system that automatically encourages user engagement and participation by routing relevant conversations to users based on individual and community activity levels. In this paper, we analyze health forum conversations from WebMD, a popular health portal consumer site, and classify them in different acts of speech using Verbal Response Modes (VRM) theory. We describe our approach for modeling an intelligent community recommender to engage participants based on observations from our analysis.

But each They are linked by an arrow which is labeled by discourse method for representing a context is quite different. Our relations R. We represent SDRS in the form of boxes like study is based on two representational methods of temporal DRS. To induce a temporal and hierarchical structure, relations: the Segmented Discourse Representation Theory SDRT distinguish discourse relations'coordinating' from (SDRT) and the model of Cognitive and Applicative'subordinating', therefore coordination and subordination Grammar (CAG). This paper presents a comparison of affect the temporal order of text: the former indicate a continuation these two approaches about aspect and tense by an analysis of some discourses pattern, like relations of'Narration' of relations between events. We are not going to show all or'Result' in discourse segmentation, and the later steps of SDRT's representations, but we take a simple discourse indicate with types of information like relations of'Elaboration' (Asher and Lascarides 2003) and we analyze the or'Explanation'. These relations are appeared same discourse with the framework of the CAG.

Temporal Nodes Bayesian Networks (TNBNs) are an alternative to Dynamic Bayesian Networks for temporal reasoning, that result in much simpler and efficient models in some domains. However, methods for learning this type of models from data have not been developed. In this paper we propose a learning algorithm to obtain the structure and temporal intervals for TNBNs from data. The method has three phases: (i) obtain an initial approximation of the intervals, (ii) obtain a structure using a standard algorithm and (iii) refine the intervals for each temporal node based on a clustering algorithm. We evaluated the method with synthetic data. Our method obtains the best score in terms of the structure and a competitive predictive accuracy.

We address a scheduling problem in the context of military aircraft maintenance where the goal is to meet the aircraft requirements for a number of missions in the presence of breakdowns. The assignment of aircraft to a mission must consider the requirements for the mission, the probability of aircraft failure, and capacity of the repair shop that maintains the aircraft. Therefore, a solution both assigns aircraft to missions and schedules the repair shop to meet the assignments. We propose a dispatching heuristic algorithm; three complete approaches based on mixed integer programming, constraint programming, and logic-based Benders decomposition; and a hybrid heuristic-complete approach. Experiments demonstrate that the logic-based Benders variation combining mixed integer programming and constraint programming outperforms the other approaches, that the dispatching heuristic can feasibly schedule the repair shop in a very short time, and that using the dispatching solution as a bound marginally improves the complete approaches.

Rook Jumping Maze design provides a number of good opportunities for experiential learning of AI concepts, including uninformed search, stochastic local search, machine learning, and objective/utility function design. In this paper we will define the maze and present a collection of exercises that allow exploration of several AI topics in the context of an engaging, fun, and unifying task.

Augmenting word tokens with a phonetic representation, derived from a dictionary, improves the performance of a Natural Language Understanding component that interprets speech recognizer output: we observed a 5% to 7% reduction in errors across a wide range of response return rates. The best performance comes from mixture models incorporating both word and phone features. Since the phonetic representation is derived from a dictionary, the method can be applied easily without the need for integration with a specific speech recognizer. The method has similarities with autonomous (or bottom-up) psychological models of lexical access, where contextual information is not integrated at the stage of auditory perception but rather later.