A Simple-to-Use BDI Architecture for Agent-Based Modeling and Simulation

With the increase of computing power and the development of user-friendly multi-agent simulation frameworks, social simulations have become increasingly realistic. However, most agent architectures in these simulations use simple reactive models. Cognitive architectures face two main obstacles: their complexity for the field-expert modeler, and their computational cost. In this paper, we propose a new cognitive agent architecture based on the Belief-Desire-Intention paradigm integrated into the GAMA modeling platform. Based on the GAML modeling language, this architecture was designed to be simple-to-use for modelers, flexible enough to manage complex behaviors, and with low computational cost. This architecture is illustrated with a simulation of the evolution of land-use in the Mekong Delta

"Hello Emily, how are you today?" Personalised dialogue in a toy to engage children

In line with the growing interest in conversational agents as companions, we are developing a toy companion for children that is capable of engaging interactions and of developing a long-term relationship with them, and is extensible so as to evolve with them. In this paper, we investigate the importance of personalising interaction both for engagement and for long-term relationship development. In particular, we propose a framework for representing, gathering and using personal knowledge about the child during dialogue interaction

A Simple-to-use BDI architecture for Agent-based Modeling and Simulation

International audienceWith the increase of computing power and the development of user-friendly multi-agent simulation frameworks, social simulations have become increasingly realistic. However, most agent architectures in these simulations use simple reactive models. Cognitive architectures face two main obstacles: their complexity for the field-expert modeler, and their computational cost. In this paper , we propose a new cognitive agent architecture based on the BDI (Belief-Desire-Intention) paradigm integrated into the GAMA modeling platform. Based on the GAML modeling language, this architecture was designed to be simple-to-use for modelers, flexible enough to manage complex behaviors, and with low computational cost. This architecture is illustrated with a simulation of the evolution of land use in the Mekong Delta

Agent oriented modelling of tactical decision making

A key requirement in military simulation is to have executable models of tactical decision-making. Such models are used to simulate the behaviour of human entities such as submarine commanders, fighter pilots and infantry, with a view to producing realistic predictions about tactical outcomes. Tactics specify the means of achieving mission objectives, and should capture both reactive and deliberative behaviour. The lack of a methodology and supporting tools for designing computer-based models of tactics makes them difficult to create, maintain and reuse, and this is now a significant problem in military simulation domains. To address this, we have developed TDF (Tactics Development Framework), a tactics modelling methodology and tool based on the BDI (Beliefs, Desires, Intentions) paradigm, that supports agent-oriented structural modelling of tactics and related artefacts including missions, storylines, goals and plans. The methodology was initially assessed by analysts in the undersea warfare domain, and was subsequently evaluated using a simple scenario in the autonomous unmanned aerial vehicles domain. The latter evaluation involved a comparison with UML designs, indicating that our methodology provides significant benefits to those building and maintaining models of tactical decision-making

A BDI agent architecture for the GAMA modeling and simulation platform

International audienceWith the increase of computing power and the development of user-friendly multi-agent simulation frameworks, social simulations have become increasingly realistic. However, most agent architectures in these simulations use simple reactive models. Indeed, cognitive agent architectures face two main obstacles: their complexity for the field-expert modeler, and their computational cost. In this paper, we propose a new cognitive agent architecture based on the BDI (Belief-Desire-Intention) paradigm integrated into the GAMA modeling platform and its GAML modeling language. This architecture was designed to be simple-to-use for modelers, flexible enough to manage complex behaviors, and with low computational cost. An experiment carried out with different profiles of end-users shows that the architecture is actually usable even by modelers who have little knowledge in programming and in Artificial Intelligence

Learning plan selection for BDI agent systems

Belief-Desire-Intention (BDI) is a popular agent-oriented programming approach for developing robust computer programs that operate in dynamic environments. These programs contain pre-programmed abstract procedures that capture domain know-how, and work by dynamically applying these procedures, or plans, to different situations that they encounter. Agent programs built using the BDI paradigm, however, do not traditionally do learning, which becomes important if a deployed agent is to be able to adapt to changing situations over time. Our vision is to allow programming of agent systems that are capable of adjusting to ongoing changes in the environment’s dynamics in a robust and effective manner. To this end, in this thesis we develop a framework that can be used by programmers to build adaptable BDI agents that can improve plan selection over time by learning from their experiences. These learning agents can dynamically adjust their choice of which plan to select in which situation, based on a growing understanding of what works and a sense of how reliable this understanding is. This reliability is given by a perceived measure of confidence, that tries to capture how well-informed the agent’s most recent decisions were and how well it knows the most recent situations that it encountered. An important focus of this work is to make this approach practical. Our framework allows learning to be integrated into BDI programs of reasonable complexity, including those that use recursion and failure recovery mechanisms. We show the usability of the framework in two complete programs: an implementation of the Towers of Hanoi game where recursive solutions must be learnt, and a modular battery system controller where the environment dynamics changes in ways that may require many learning and relearning phases

Expedition 357 methods

This chapter documents the primary procedures and methods employed by the operational and scientific groups during the offshore and onshore phases of International Ocean Discovery Program (IODP) Expedition 357. This information concerns only shipboard and Onshore Science Party (OSP) methods described in the site chapters. Methods for postexpedition research conducted on Expedition 357 samples and data will be described in individual scientific contributions. Detailed drilling and engineering operations are described in the Operations section of each site chapter

NASA Space Engineering Research Center for utilization of local planetary resources

The University of Arizona and NASA have joined to form the UA/NASA Space Engineering Research Center. The purpose of the Center is to discover, characterize, extract, process, and fabricate useful products from the extraterrestrial resources available in the inner solar system (the moon, Mars, and nearby asteroids). Individual progress reports covering the center's research projects are presented and emphasis is placed on the following topics: propellant production, oxygen production, ilmenite, lunar resources, asteroid resources, Mars resources, space-based materials processing, extraterrestrial construction materials processing, resource discovery and characterization, mission planning, and resource utilization

Conférence Nationale d'Intelligence Artificielle Année 2020

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