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Author

Amel Ben Othmane

Bio: Amel Ben Othmane is an academic researcher. The author has contributed to research in topics: Agent architecture & Unexpected events. The author has co-authored 1 publications.

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Dissertation
12 Oct 2017
TL;DR: A formal framework for recommending plans to agents in the decision making process, when they deal with uncertain spatio-temporal information is introduced and evaluated through an Agent Based Simulation (ABS) in a real-world traffic scenario.
Abstract: Recently, many real-world applications where different entities interact in a dynamic environment, consider the use of agents in their architectures due principally to their autonomy, reactivity and decision-making abilities. Though these systems can be made intelligent, using Artificial Intelligence (AI) techniques, agents still lack of social abilities and have limited knowledge of their environment and in particular when it comes to a dynamic environment. In fact, when operating in the real world, agents need to deal with unexpected events considering both changes in time and space. Moreover, agents must face the uncertainty which pervades real-world scenarios in order to provide an accurate representation of the world. In this thesis, we introduce and evaluate a formal framework for recommending plans to agents in the decision making process, when they deal with uncertain spatio-temporal information. The agent-based architecture we propose to address this issue, called CARS (Cognitive Agent-based Recommender System), has been designed by extending the well known Belief-Desire-Intention (BDI) architecture to incorporate further capabilities to support reasoning with different types of contextual information, including the social context. Uncertainty on the agent's beliefs, desires and intentions is modeled using possibility theory. To meet the requirements of real-world applications, e.g., traffic and navigation recommendation systems, we de ne a spatio-temporal representation of the agents' beliefs and intentions. Using such a formal framework, anticipatory reasoning about intentional dynamics can be performed with the aim to recommend an optimal plan to a certain user. Since spatio-temporal data is often considered as incomplete and/or vague, we extended the formal framework with a fuzzy representation of spatio-temporal beliefs and intentions. The framework is evaluated through an Agent Based Simulation (ABS) in a real-world traffic scenario. This ABS allowed us to create a virtual environment to test the impact of the different features of our framework as well as to evaluating the main strengths and weaknesses of the proposed agent architecture.