Academic literature on the topic 'Belief-desire-intention'
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Journal articles on the topic "Belief-desire-intention"
Mele, Alfred R. "Against a belief/desire analysis of intention." Philosophia 18, no. 2-3 (July 1988): 239–42. http://dx.doi.org/10.1007/bf02380079.
Full textWadsley, Theo, and Malcolm Ryan. "A Belief-Desire-Intention Model for Narrative Generation." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 9, no. 4 (June 30, 2021): 105–8. http://dx.doi.org/10.1609/aiide.v9i4.12627.
Full textSaadi, Adel, Ramdane Maamri, and Zaidi Sahnoun. "Behavioral flexibility in Belief-Desire- Intention (BDI) architectures." Multiagent and Grid Systems 16, no. 4 (December 31, 2020): 343–77. http://dx.doi.org/10.3233/mgs-200335.
Full textSinhababu, Neil. "The Desire-Belief Account of Intention Explains Everything." Noûs 47, no. 4 (June 21, 2012): 680–96. http://dx.doi.org/10.1111/j.1468-0068.2012.00864.x.
Full textChen, Huang, Chen Long, and Hao-Bin Jiang. "Building a Belief–Desire–Intention Agent for Modeling Neural Networks." Applied Artificial Intelligence 29, no. 8 (September 14, 2015): 753–65. http://dx.doi.org/10.1080/08839514.2015.1071089.
Full textKardas, Geylani, Baris Tekin Tezel, and Moharram Challenger. "Domain‐specific modelling language for belief–desire–intention software agents." IET Software 12, no. 4 (August 2018): 356–64. http://dx.doi.org/10.1049/iet-sen.2017.0094.
Full textKoo, Chulmo, Youhee Joun, Heejeong Han, and Namho Chung. "A structural model for destination travel intention as a media exposure." International Journal of Contemporary Hospitality Management 28, no. 7 (July 11, 2016): 1338–60. http://dx.doi.org/10.1108/ijchm-07-2014-0354.
Full textFarrell, Rachelyn. "Experience Management with Beliefs, Desires, and Intentions for Virtual Agents." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 14, no. 1 (September 25, 2018): 290–92. http://dx.doi.org/10.1609/aiide.v14i1.13006.
Full textOrtiz-Hernández, Gustavo, Alejandro Guerra-Hernández, Jomi F. Hübner, and Wulfrano Arturo Luna-Ramírez. "Modularization in Belief-Desire-Intention agent programming and artifact-based environments." PeerJ Computer Science 8 (December 1, 2022): e1162. http://dx.doi.org/10.7717/peerj-cs.1162.
Full textKashima, Yoshihisa, Allison McKintyre, and Paul Clifford. "The Category of the Mind: Folk Psychology of Belief, Desire, and Intention." Asian Journal Of Social Psychology 1, no. 3 (December 1998): 289–313. http://dx.doi.org/10.1111/1467-839x.00019.
Full textDissertations / Theses on the topic "Belief-desire-intention"
Yao, Yuan. "Robust execution of belief-desire-intention-based agent programs." Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/46948/.
Full textLee, Seung Ho. "INTEGRATED HUMAN DECISION BEHAVIOR MODELING UNDER AN EXTENDED BELIEF-DESIRE-INTENTION FRAMEWORK." Diss., The University of Arizona, 2009. http://hdl.handle.net/10150/193788.
Full textCoelho, Cássio Giorgio Couto. "Agentes racionais baseados no modelo belief-desire-intention para o sistema multiagente MASE." reponame:Repositório Institucional da UnB, 2014. http://repositorio.unb.br/handle/10482/17448.
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MASE, acrônimo para Multi-Agent System for Enviromental Simulation, foi uma aplicação desenvolvida para a investigação da dinâmica do uso e conversão do solo em cenários ambientais, e apresentou bons resultados utilizando o modelo Cerrado-DF. Como forma de aumentar o domínio dessa ferramenta, este trabalho explorou o modelo de cognição baseado em Belief-Desire-Intention por meio do framework JADEX. Para isso, a arquitetura do MASE foi reformulada e seu código foi refatorado, tanto para que os agentes representassem melhor o raciocínio humano quanto para que a aplicação possuísse melhor desempenho de tempo na execução das simulações. A evolução dessas características trouxe o sucessor do MASE, que foi validado nesse trabalho por meio de dois estudos de caso. Os resultados gerados com essa nova proposta foram comparados com os obtidos no MASE, testando assim a exibilidade da ferramenta e a melhoria do desempenho do sistema. ____________________________________________________________________________________ ABSTRACT
MASE, acronym to Multi-Agent System for Enviromental Simulation, was an application developed for land usage and cover change dynamics investigation, using diferent environmental scenarios, and good results with the Cerrado-DF model were obtained with its usage. To increase the domain of MASE, this work explored the Belief-Desire- Intention cognition model using the JADEX framework. This objective was obtained by MASE architecture reformulation, with code refactoring, so the agents could better represent human rationality, as the system time performance could be enhanced. The evolution of this features brought MASE's sucessor: MASE-BDI, which was validated by two case studies. The generated results were compared with the ones obtained in the past with MASE, so the MASE-BDI _exibility could be tested, as performance enhance could be proved as well.
Kim, Sojung. "Dynamic Learning and Human Interactions under the Extended Belief-Desire-Intention Framework for Transportation Systems." Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/578837.
Full textTIENGO, Willy Carvalho. "Assistente avançado de suporte ao motorista para redução de risco de tombamento de veículos pesados em curva." Universidade Federal de Campina Grande, 2018. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/566.
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No Brasil, o transporte rodoviário é responsável por 58% do transporte de carga, que tem os acidentes como um grande problema, pois, em geral, esses ocasionam muitas vítimas, prejuízos econômicos relevantes e em alguns casos danos ambientais decorrentes de derramamento de carga. Estudos apontam que os prejuízos com os acidentes no transporte de carga em 2012 foram de mais de 9 bilhões de reais. Estudo realizado em 2007 pela PAMCARY, corretora de seguros e gestora de riscos, revelou que os eventos que combinam maior frequência e gravidade são tombamento e capotagem. Nesse sentido, esta pesquisa consiste na elaboração de um assistente avançado para motorista que objetiva alertar previamente sobre a velocidade limite da curva, a fim de diminuir os riscos de tombamento. Em outras palavras, consiste em buscar mitigar o problema auxiliando o motorista para que ele mantenha o veículo em uma velocidade segura, por meio de alertas e em prazo adequado, que permitam ao motorista tomar medidas corretivas em caso de estado inseguro. A solução foi desenvolvida a partir de uma arquitetura modular, que funciona da seguinte forma: por meio de sensores (velocidade, GPS e posição do acelerador), associado a mapas digitais, o risco de acidente é controlado constantemente. Com isso, um dispositivo poderia ser embarcado na cabine do veículo para emitir alertas visual e auditivo de risco de tombamento. A solução utiliza o indicador de estabilidade chamado Limiar Estático de Tombamento que, associado à informação a priori de mapas digitais, permite o cálculo do risco de tombamento com diferentes abordagens. No contexto da pesquisa, foram desenvolvidas 04 versões de assistentes. Além disso, foi proposto um arcabouço de simulação microscópica de trânsito baseado no modelo de raciocínio prático denominado de belief-desire-intention (BDI) para permitir o desenvolvimento e a validação de agentes inteligentes para Sistemas Avançados de Assistência ao Motorista de maneira rápida, flexível e fácil. Para avaliar o potencial dos assistentes, foi escolhida a BR-101, estrada federal de Alagoas com mais ocorrências de tombamento. Nessa rodovia, foram simulados 400 veículos para avaliar o desempenho dos assistentes propostos. Em particular, foram investigadas a efetividade, intrusividade, omissão e a segurança para avaliar o desempenho dos assistentes.
In Brazil, highway transportation is responsible for 58% of cargo transport. A relevant problem associated to cargo transport are the accidents, that generally cause an elevated number of victims, relevant economic losses and, in some cases, damages to the environment due to cargo spills, since there are also dangerous products being transported. Researches point out that the cost of accidents in cargo transportation in 2012 was more than BRL 9 billion. A study performed in 2007 by PAMCARY revealed the accidents profile: the events that combine higher frequency and gravity are rollover and tipping (considered here as the same nature). In this study, incompatible speed and fatigue, factors that are related to human actions, were pointed out as main causes of accidents; for another hand, sharp curve and poorly maintained roads are contributing factors to accidents. Therefore, the research proposal consists of the adoption of an assistant for warning in advance of over speed for a specific curve. This may reduce rollover risks. In other words, it would be mitigated the problem by helping the driver to maintain the vehicle in a safe speed, through customized alerts just in time to allow the driver to take corrective maneuvers in case of unsafe state. The solution is a modular architecture, which works as follows: through sensors (speed, GPS and throttle position) associated with digital maps, it is controlled the risk of accident constantly. With that, an embedded device at the vehicle’s cab could to emit visual and sound alerts warning the risk of rollover. In this work, it is proposed the adoption of the stability indicator known as Static Rollover Threshold, which is combined with a priori information from digital maps to allow the calculation of the rollover risk by different approaches. In the context of this research, 04 versions of assistants were developed. In addition, a microscopic traffic simulation framework was proposed based on the practical reasoning model named belief-desire-intention (BDI) to support the development and validation of intelligent agents for Advanced Driver Assistance Systems in a fast, flexible and easy way. To evaluate the assistants’ potential, the BR-101, Federal Highway of Alagoas with more occurrence of rollover, was chosen. On this highway, 400 vehicles were simulated to evaluate the performance of the proposed assistants. The effectiveness, intrusiveness, omission and safety of the assistants were investigated.
Nair, Vineet. "On Extending BDI Logics." Thesis, Griffith University, 2003. http://hdl.handle.net/10072/365892.
Full textThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Information Technology
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Nair, Vineet, and n/a. "On Extending BDI Logics." Griffith University. School of Information Technology, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030929.095254.
Full textStenfelt, Matilda. "Intentionalitet i kollektiva beteenden hos en artificiell svärm." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166315.
Full textBosello, Michael. "Integrating BDI and Reinforcement Learning: the Case Study of Autonomous Driving." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21467/.
Full textLiu, Bang-Quan, and 劉邦權. "Using Belief-Desire-Intention Agent for ebXML CPA Negotiation." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/54833460168930719888.
Full text中原大學
資訊管理研究所
93
The ebXML is one of the most important frameworks for electronic commerce disciplines. Before trading, business partners have proposed the collaboration protocol profile (CPP) to describe their business capabilities. In practice, two enterprises base on their CPP to negotiate an agreement in terms of a collaboration protocol agreement (CPA). Currently the negotiation process is highly human-intensive that causes some problematic such as time consuming and error prone. Since ebXML intend to provide automated business framework, the efficient performance in preprocess of ebXML framework is essential. This study proposes a synergy approach based on the agent mechanism. The core architecture of the agent utilizes the Belief-Desire-Intention (BDI) to construct rules and actions in terms of the negotiation protocol. The BDI is intention centered that facilitates the internal design becoming consequence-reason. The study provides a walkthrough example to verify BDI-based agents in negotiation. The empirical feedbacks indicate that BDI has advantages in reducing the complexity of negotiation protocol and raising negotiation performance.
Books on the topic "Belief-desire-intention"
Simari, Gerardo I., and Simon D. Parsons. Markov Decision Processes and the Belief-Desire-Intention Model. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1472-8.
Full textD, Parsons Simon, and SpringerLink (Online service), eds. Markov Decision Processes and the Belief-Desire-Intention Model: Bridging the Gap for Autonomous Agents. New York, NY: The Author(s), 2011.
Find full textByrne, Alex. Desire, Intention, and Emotion. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198821618.003.0007.
Full textSalice, Alessandro. Practical Intentionality. Edited by Dan Zahavi. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198755340.013.7.
Full textWilliamson, Timothy. Acting on Knowledge. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198716310.003.0008.
Full textWilliams, J. Robert G. The Metaphysics of Representation. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198850205.001.0001.
Full textByrne, Alex. Transparency and Self-Knowledge. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198821618.001.0001.
Full textBook chapters on the topic "Belief-desire-intention"
Sacharny, David, and Thomas Henderson. "UAS Belief–Desire–Intention Agent Architecture." In Lane-Based Unmanned Aircraft Systems Traffic Management, 83–108. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-030-98574-5_6.
Full textGeorgeff, Michael, Barney Pell, Martha Pollack, Milind Tambe, and Michael Wooldridge. "The Belief-Desire-Intention Model of Agency." In Intelligent Agents V: Agents Theories, Architectures, and Languages, 1–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-49057-4_1.
Full textFichera, Loris, Daniele Marletta, Vincenzo Nicosia, and Corrado Santoro. "Flexible Robot Strategy Design Using Belief-Desire-Intention Model." In Research and Education in Robotics - EUROBOT 2010, 57–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27272-1_5.
Full textSu, Kaile, Weiya Yue, Abdul Sattar, Mehmet A. Orgun, and Xiangyu Luo. "Observation-Based Logic of Knowledge, Belief, Desire and Intention." In Knowledge Science, Engineering and Management, 366–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11811220_31.
Full textSimari, Gerardo I., and Simon D. Parsons. "Introduction." In Markov Decision Processes and the Belief-Desire-Intention Model, 1–2. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1472-8_1.
Full textSimari, Gerardo I., and Simon D. Parsons. "Preliminary Concepts." In Markov Decision Processes and the Belief-Desire-Intention Model, 3–9. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1472-8_2.
Full textSimari, Gerardo I., and Simon D. Parsons. "An Empirical Comparison of Models." In Markov Decision Processes and the Belief-Desire-Intention Model, 11–25. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1472-8_3.
Full textSimari, Gerardo I., and Simon D. Parsons. "A Theoretical Comparison of Models." In Markov Decision Processes and the Belief-Desire-Intention Model, 27–48. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1472-8_4.
Full textSimari, Gerardo I., and Simon D. Parsons. "Related Work." In Markov Decision Processes and the Belief-Desire-Intention Model, 49–53. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1472-8_5.
Full textSimari, Gerardo I., and Simon D. Parsons. "Conclusions, Limitations, and Future Directions." In Markov Decision Processes and the Belief-Desire-Intention Model, 55–56. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1472-8_6.
Full textConference papers on the topic "Belief-desire-intention"
Su, Kaile, Xiangyu Luo, Abdul Sattar, and Mehmet A. Orgun. "The interpreted system model of knowledge, belief, desire and intention." In the fifth international joint conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1160633.1160668.
Full textSon, Young-Jun. "An Integrated Human Decision Making Model under Extended Belief-Desire-Intention Framework." In SIGSIM-PADS '17: SIGSIM Principles of Advanced Discrete Simulation. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3064911.3064936.
Full textMateus, Gustavo Pereira, Beatriz Wilges, Luiz Claudio Duarte Dalmolin, Silvia Nassar, and Ricardo Silveira. "A Belief Desire Intention Multi Agent System in a Virtual Learning Environment." In 2009 Ninth IEEE International Conference on Advanced Learning Technologies (ICALT). IEEE, 2009. http://dx.doi.org/10.1109/icalt.2009.213.
Full textLee, Seungho, and Young-Jun Son. "Integrated human decision making model under Belief-Desire-Intention framework for crowd simulation." In 2008 Winter Simulation Conference (WSC). IEEE, 2008. http://dx.doi.org/10.1109/wsc.2008.4736153.
Full textShaw, Gail, and Etienne van der Poel. "Genetic Algorithms as a feasible re-planning mechanism for Belief-Desire-Intention Agents." In the 2015 Annual Research Conference. New York, New York, USA: ACM Press, 2015. http://dx.doi.org/10.1145/2815782.2815817.
Full textKim, Sojung, Young-Jun Son, Ye Tian, and Yi-Chang Chiu. "Drivers' en-route divergence behavior modeling using Extended Belief-Desire-Intention (E-BDI) framework." In 2014 Winter Simulation Conference - (WSC 2014). IEEE, 2014. http://dx.doi.org/10.1109/wsc.2014.7019901.
Full textVanegas-Hernandez, Meili, Célia da Costa Pereira, Diego Moreno, Giovanni Fusco, Andrea G. B. Tettamanzi, Michel Riveill, and José Tiberio Hernández. "A new urban segregation-growth coupled model using a belief-desire-intention possibilistic framework." In WI '17: International Conference on Web Intelligence 2017. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3106426.3106486.
Full textXiao, Zhanhao. "Refinement of Intentions." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/771.
Full textYao, Yuan, Natasha Alechina, Brian Logan, and John Thangarajah. "Intention Progression under Uncertainty." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/2.
Full textJiaqi Yan, Shanshan Wang, S. X. Sun, Huaiqing Wang, and Zhongsheng Hua. "A Belief-Desire-Intention logic model for analysing the cheating behaviour in quality control of dairy product." In 2009 3rd IEEE International Conference on Digital Ecosystems and Technologies (DEST). IEEE, 2009. http://dx.doi.org/10.1109/dest.2009.5276769.
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