A Fuzzy Belief-Desire-Intention Model for Agent-Based Image Analysis

Recent methods of image analysis in remote sensing lack a sufficient grade of robustness and transferability. Methods such as object-based image analysis (OBIA) achieve satisfying results on single images. However, the underlying rule sets for OBIA are usually too complex to be directly applied on a variety of image data without any adaptations or human interactions. Thus, recent research projects investigate the potential for integrating the agent-based paradigm with OBIA. Agent-based systems are highly adaptive and therefore robust, even under varying environmental conditions. In the context of image analysis, this means that even if the image data to be analyzed varies slightly (e.g., due to seasonal effects, different locations, atmospheric conditions, or even a slightly different sensor), agent-based methods allow to autonomously adapt existing analysis rules or segmentation results according to changing imaging situations. The basis for individual software agents’ behavior is a so-called believe-desire-intention (BDI) model. Basically, the BDI describes for each individual agent its goal(s), its assumed current situation, and some action rules potentially supporting each agent to achieve its goals. The chapter introduces a believe-desire-intention (BDI) model based on fuzzy rules in the context of agent-based image analysis, which extends the classic OBIA paradigm by the agent-based paradigm

Negociación entre agentes intencionales: propuesta basada en revisión de creencias

En los últimos años, el trabajo en el área de negociación automática de agentes inteligentes a ganado mucha atención dentro de la comunidad de sistemas multiagentes (MAS), Un escenario típico para la negociación se da cuando los agentes no pueden alcanzar sus objetivos por sí mismos, y tienen que intercambiar recursos (objetos, conocimientos, etc) para llegar a cumplirlos. Diversas técnicas para modelar tales escenarios se han propuesto en la comunidad, incluyendo el uso de arquitecturas BDI, la lógica de la argumentación, y la programación n lógica. La línea de trabajo propuesta, presenta un enfoque novedoso de negociación automatizada basada en operadores revisión de creencias. A medida que avanza la negociación, los agentes generan propuestas que ellos creen que son soluciones al problema. Durante este proceso las creencias de los agentes se actualizan a medida que el dialogo ocurre entre los agentes. El enfoque propuesto establece un escenario cooperativo, una arquitectura de agente intencional y un modelo computacional del motor de decisión de cada agente, encargado de evaluar la propuesta recibida y de generar la respuesta a presentar. Se ha desarrollado un algoritmo de alto nivel de este modelo y se lo ha implementado utilizando programación lógica.Eje: Agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Designing a BDI agent model for behavioural change process

Belief-Desire-Intention (BDI) model is well suited for describing agent’s mental state.The BDI of an agent represents its motivational stance and are the main determinant of agent’s actions. Therefore, explicit understanding of the representation and modelling of such motivational stance plays a central role in designing BDI agent with successful behavioural change interventions.Nevertheless, existing BDI agent models do not represent agent’s behavioural factors explicitly.This leads to a gap between design and implementation where psychological reactance has being identified as the cause of BDI agent behavioural change interventions failure. Hence, this paper presents a generic representation of BDI agent model based on behavioural change and psychological theories.The objective of this proposed BDI agent model is to bridge the gap between agent design and implementation for successful agent-based interventions.The model will be realized in an agent-based application that motivates children towards oral hygiene

Designing a BDI agent reactant model of behavioural change intervention

Belief-Desire-Intention (BDI) model is well suited for describing agent’s mental state. The BDI of an agent represents its motivational stance and are the main determinant of agent’s actions.Therefore, explicit understanding of the representation and modelling of such motivational stance plays a central role in designing BDI agent with successful behavioral change interventions. Nevertheless, existing BDI agent models do not represent agent’s behavioral factors explicitly. This leads to a gap between design and implementation where psychological reactance has being identified as the cause of BDI agent behavioral change interventions failure. Hence, this paper presents a generic representation of BDI agent model based on behavioral change and psychological theories.Also, using mathematical analysis the model was evaluated. The objective of the proposed BDI agent model is to bridge the gap between agent design and implementation for successful agent-based interventions.The model will be realized in an agent based application that motivates children towards oral hygiene. The study explicitly depicts how agent’s behavioral factors interact to enhance behavior change which will assist agent-based intervention designers to be able to design intervention that will be void of reactance

Multi-criteria argumentation-based decision making within a BDI agent

The BDI model, as a practical reasoning architecture aims at making decisions about what to do based on cognitives notions as beliefs, desires and intentions. However, during the decision making process, BDI agents also have to make background decisions like choosing what intention to achieve next from a set of possibly con icting desires; which plan to execute from among the plans that satisfy a given intention; and whether is necessary or not to reconsider current intentions. With this aim, in this work, we present an abstract framework which integrates a Possibilistic Defeasible Logic Programming approach to decision making in the inner decision processes within BDI agents.XIV Workshop agentes y sistemas inteligentes.Red de Universidades con Carreras en Informática (RedUNCI

Weighted logics for artificial intelligence : an introductory discussion

International audienceBefore presenting the contents of the special issue, we propose a structured introductory overview of a landscape of the weighted logics (in a general sense) that can be found in the Artificial Intelligence literature, highlighting their fundamental differences and their application areas

Reasoning about constitutive norms in BDI agents

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Logic Journal of the IGPL following peer review. The definitive publisher-authenticated version: Criado Pacheco, N.; Argente Villaplana, E.; Noriega, P.; Botti Navarro, VJ. (2014). Reasoning about constitutive norms in BDI agents. Logic Journal of the IGPL. 22(1):66-93 is available online at: http://dx.doi.org/1093/jigpal/jzt035Software agents can be members of different institutions along their life; they might even belong to different institutions simultaneously. For these reasons, agents need capabilities that allow them to determine the repercussion that their actions would have within the different institutions. This association between the physical word, in which agents interactions and actions take place, and the institutional world is defined by means of constitutive norms. Currently, the problem of how agents reason about constitutive norms has been tackled from a theoretical perspective only. Thus, there is a lack of more practical proposals that allow the development of software agents capable of reasoning about constitutive norms. In this article we propose an information model, knowledge representation and an inference mechanism to enable Belief-Desire-Intention agents to reason about the consequences of their actions on the institutions and making decisions accordingly. Specifically, the information model, knowledge representation and inference mechanism proposed in this article allows agents to keep track of the institutional state given that they have a physical presence in some real-world environment. Agents have a limited and not fully believable knowledge of the physical world (i.e. they are placed in an uncertain environment). Therefore, our proposal also deals with the uncertainty of the environment.Criado Pacheco, N.; Argente Villaplana, E.; Noriega, P.; Botti Navarro, VJ. (2014). Reasoning about constitutive norms in BDI agents. Logic Journal of the IGPL. 22(1):66-93. doi:10.1093/jigpal/jzt035S6693221Baldi, P., Brunak, S., Chauvin, Y., Andersen, C. A. F., & Nielsen, H. (2000). Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics, 16(5), 412-424. doi:10.1093/bioinformatics/16.5.412Bloch, I. (1996). Information combination operators for data fusion: a comparative review with classification. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 26(1), 52-67. doi:10.1109/3468.477860Casali, A., Godo, L., & Sierra, C. (2011). A graded BDI agent model to represent and reason about preferences. Artificial Intelligence, 175(7-8), 1468-1478. doi:10.1016/j.artint.2010.12.006Criado, N., Julián, V., Botti, V., & Argente, E. (2010). A Norm-Based Organization Management System. Lecture Notes in Computer Science, 19-35. doi:10.1007/978-3-642-14962-7_2Governatori, G., & Rotolo, A. (2008). BIO logical agents: Norms, beliefs, intentions in defeasible logic. Autonomous Agents and Multi-Agent Systems, 17(1), 36-69. doi:10.1007/s10458-008-9030-4Grossi, D., Aldewereld, H., Vázquez-Salceda, J., & Dignum, F. (2006). Ontological aspects of the implementation of norms in agent-based electronic institutions. Computational & Mathematical Organization Theory, 12(2-3), 251-275. doi:10.1007/s10588-006-9546-6Hübner, J. F., Boissier, O., Kitio, R., & Ricci, A. (2009). Instrumenting multi-agent organisations with organisational artifacts and agents. Autonomous Agents and Multi-Agent Systems, 20(3), 369-400. doi:10.1007/s10458-009-9084-yJONES, A. J. I., & SERGOT, M. (1996). A Formal Characterisation of Institutionalised Power. Logic Journal of IGPL, 4(3), 427-443. doi:10.1093/jigpal/4.3.427Rawls, J. (1955). Two Concepts of Rules. The Philosophical Review, 64(1), 3. doi:10.2307/2182230Da Silva, V. T. (2008). From the specification to the implementation of norms: an automatic approach to generate rules from norms to govern the behavior of agents. Autonomous Agents and Multi-Agent Systems, 17(1), 113-155. doi:10.1007/s10458-008-9039-

等级BDI逻辑:关于行为表征的柔性逻辑

“行动“或“行为(ACTIOn)“是哲学、心理学、历史、法律、经济、军事、计算机科学和人工智能等诸多学科研究的核心概念,这是因为行动在这些领域中无处不在。计算机通过执行以某种程序设计语言编写的程序语句来完成行动,并据此改变计算机的内部世界,然后通过与外部世界的接口来改变外部世界。一个程序的执行和调用本质上就是一个行动。对于具有自我意识的机器人而言:行动或行为就是国家社会科学基金重点项目“现代逻辑视野的认知研究”(11AZD057); 国家哲学社会科学基金项目“基于逻辑视域的认知研究”(11BZX062)的资

A framework for multi-criteria argumentation-based decision making within a BDI agent

The BDI model, as a practical reasoning architecture aims at making decisions about what to do based on cognitives notions as beliefs, desires and intentions. However, during the decision making process, BDI agents also have to make background decisions like choosing what intention to achieve next from a set of possibly conflicting desires; which plan to execute from among the plans that satisfy a given intention; and whether is necessary or not to reconsider current intentions. With this aim, in this work, we present an abstract framework which integrates a Possibilistic Defeasible Logic Programming approach to decision making in the inner decision processes within BDI agents.Facultad de Informátic

A graded BDI agent model to represent and reason about preferences

In this research note, we introduce a graded BDI agent development framework, g-BDI for short, that allows to build agents as multi-context systems that reason about three fundamental and graded mental attitudes (i.e. beliefs, desires and intentions). We propose a sound and complete logical framework for them and some logical extensions to accommodate slightly different views on desires. © 2011 Elsevier B.V. All rights reserved.The authors acknowledge partial support of the Spanish project AT (Consolider CSD2007-022, Ingenio 2010)Peer Reviewe