Perception Policies for Intelligent Virtual Agents
Abstract Agents deployed to dynamic environments, such as virtual and augmented reality, need specific mechanisms to capture relevant features from the environment. These mechanisms enable agents to avoid process some useless information and act quickly. The primary goal of this work is to investigate the perception policies of an agent situated in a virtual environment. Perception policies allow giving more priority to sensors perceiving the changes occurring in the environment. Based on the proposed model, each sensor follows a strategy that can change its priority in the overall system. We developed two policies to change the sensors prioritization. The performance evaluation of the proposed model consists of comparing both approaches in a highly dynamic environment.
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Rafaeli, N. and Kaminka, G. A., 2017. Active Perception at the Architecture Level. In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, pages 1708–1710. International Foundation for Autonomous Agents and Multiagent Systems.
So, R. and Sonenberg, L., 2009. The Roles of Active Perception in Intelligent Agent Systems. In Multi-Agent Systems for Society: 8th Pacific Rim International Workshop on Multi-Agents, PRIMA 2005, Kuala Lumpur, Malaysia, September 26-28, 2005, Revised Selected Papers, volume 4078, page 139. Springer Science & Business Media.
Stabile, J., M.F. and Sichman, J., 2016. Evaluating Perception Filters in BDI Jason Agents. pages 116–121. doi:10.1109/BRACIS.2015.18. Unity Technologies, 2019. Unity 3D.
Van Oijen, J. and Dignum, F., 2011. Scalable perception for BDI-agents embodied in virtual environments. volume 2, pages 46–53. doi:10.1109/WI-IAT.2011.176.
Weyns, D., Steegmans, E., and Holvoet, T., 2004. Towards active perception in situated multi-agent systems.Applied Artificial Intelligence, 18(9-10):867–883.
Brewka, G., Eiter, T., and Fink, M., 2011. Nonmonotonic multi-context systems: A flexible approach for integrating heterogeneous knowledge sources. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6565 LNAI:233–258. ISSN 03029743. doi:10.1007/978-3-642-20832-4_16.
Casali, A., Godo, L., and Sierra, C., 2005. Graded BDI Models for Agent Architectures, volume 3487.
Corchado, J. M., Bajo, J., De Paz, Y., and Tapia, D. I., 2008. Intelligent environment for monitoring Alzheimer patients, agent technology for health care. Decision Support Systems, 44(2):382–396.
Endsley, M. R., 1988. Situation awareness global assessment technique (SAGAT). In Aerospace and Electronics Conference, 1988. NAECON 1988., Proceedings of the IEEE 1988 National, pages 789–795. IEEE.
Farias, G., Dimuro, G., and Rocha Costa, A., 2010. BDI agents with fuzzy perception for simulating decision making in environments with imperfect information. volume 627.
Gelaim, T. Â., Hofer, V. L., Marchi, J., and Silveira, R. A., 2019. Sigon: A multi-context system framework for intelligent agents. Expert Systems with Applications, 119:51–60.
Gibson, J. J., 1950. The perception of the visual world.
Giunchiglia, F. and Serafini, L., 1994. Multilanguage hierarchical logics, or: how we can do without modal logics. Artificial intelligence, 65(1):29–70.
Herrero, P., Greenhalgh, C., and De Antonio, A., 2005. Modelling the sensory abilities of intelligent virtual agents. Autonomous Agents and Multi-Agent Systems, 11(3):361–385. ISSN 13872532. doi:10.1007/ s10458-005-2921-8.
Mello, R. R. P., Gelaim, T. A., and Silveira, R. A., 2018. Negotiating Agents: A Model Based on BDI Architecture and Multi-Context Systems Using Aspiration Adaptation Theory as a Negotiation Strategy. In Proceedings of the 12th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2018). Springer International Publishing, Matsue, Japan.
Rafaeli, N. and Kaminka, G. A., 2017. Active Perception at the Architecture Level. In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, pages 1708–1710. International Foundation for Autonomous Agents and Multiagent Systems.
So, R. and Sonenberg, L., 2009. The Roles of Active Perception in Intelligent Agent Systems. In Multi-Agent Systems for Society: 8th Pacific Rim International Workshop on Multi-Agents, PRIMA 2005, Kuala Lumpur, Malaysia, September 26-28, 2005, Revised Selected Papers, volume 4078, page 139. Springer Science & Business Media.
Stabile, J., M.F. and Sichman, J., 2016. Evaluating Perception Filters in BDI Jason Agents. pages 116–121. doi:10.1109/BRACIS.2015.18. Unity Technologies, 2019. Unity 3D.
Van Oijen, J. and Dignum, F., 2011. Scalable perception for BDI-agents embodied in virtual environments. volume 2, pages 46–53. doi:10.1109/WI-IAT.2011.176.
Weyns, D., Steegmans, E., and Holvoet, T., 2004. Towards active perception in situated multi-agent systems.Applied Artificial Intelligence, 18(9-10):867–883.
Souza De Freitas, G., Gelaim, T. Ângelo, Rodrigues Pires De Mello, R., & Azambuja Silveira, R. (2019). Perception Policies for Intelligent Virtual Agents. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 8(2), 87–95. https://doi.org/10.14201/ADCAIJ2019828795
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