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Last update: RNDr. Tomáš Holan, Ph.D. (04.01.2024)
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Last update: RNDr. Tomáš Holan, Ph.D. (04.01.2024)
To give the student an overview about artificial beings as embodied intelligent agents, whose decision-making is subject to the bounded rationality. |
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Last update: RNDr. Tomáš Holan, Ph.D. (04.01.2024)
To complete the course, the student has to receive a credit from labs by solving assignments and then pass the practical examination. |
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Last update: RNDr. Tomáš Holan, Ph.D. (04.01.2024)
Bratman, M. (1999). Intention, plans, and practical reason. Center for the Study of Language and Information. Brooks, A. R.: Intelligence without reason. In: Proceedings of the 1991 International Joint Conference on Artificial Intelligence, Sydney (1991) 569-595 Bryson, J.: Hierarchy and sequence vs. full parallelism in reactive action selection architecture. In: From Animals to Animats (SAB00). MA. MIT Press, Cambridge (2000) 147-156 Černý, M., Plch, T., Marko, M., Ondráček, P., & Brom, C. (2014). Smart Areas: A Modular Approach to Simulation of Daily Life in an Open World Video Game. 6th International Conference on Agents and Artificial Intelligence (ICAART 2014), 703-708. Edelstein-Keshet, L: Mathematical Models in Biology. SIAM (2005) (Ch. 4.1, 4.2, 6.1 - 6.3) Fu, D., & Houlette, R. (2004). The Ultimate Guide to FSMs in Games. In S. Rabin (Ed.), AI Game Programming Wisdom (First, Vol. 2, pp. 283-302). Massachusetts, USA: Charles River Media. Grand, S., Cliff, D., Malhotra, A.: Creatures: Artificial life autonomous software-agents for home entertainment. In: Lewis Johnson, W. (eds.): Proceedings of the First International Conference on Autonomou Agents. ACM press (1997) 22-29 Hindriks KV, (2009). Programming Rational Agents in GOAL, Multi-Agent Programming: Languages and Tools and Applications, Springer US, pages:119-157, isbn: 978-0-387-89298-6 Huber, M. J.: JAM: A BDI-theoretic mobile agent architecture. In: Proceedings of the Third International Conference on Autonomous Agents (Agents'99). Seatle (1999) 236-243 Champandard, A. J. (2008). Behavior Trees for Next-Gen Game AI [Video]. Retrieved from http://aigamedev.com/insider/presentations/behavior-trees [17.5.2017] Kokko, H.: Modelling for Field Biologists and Other Interesting People. Cambridge University Press (2007) Laird, J. E., Newell, A., Rosenbloom, P.S.: SOAR: An Architecture for General Intelligence. In: Artificial Intelligence, 33(1) (1987) 1-64 Mateas, M.: Interactive Drama, Art and Artificial Intelligence. Ph.D. Dissertation. Department Computer Science, Carnegie Mellon University (2002) viz též: https://eis-blog.soe.ucsc.edu/2012/02/getting-started-with-abl/ Orkin, J. (2006). Three States and a Plan: The AI of F.E.A.R. In Proceedings of the Game Developers Conference (GDC). Rao, A. S., & Georgeff, M. P. (1995). BDI Agents: From Theory to Practice. Proceedings of the First International Conference on Multi-Agent Systems (ICMAS-95), San Francisco, USA, 1995, 312--319. Rabin, S. (ed.): AI Game Programming Wisdom I - IV, Charles River Media (2002 - 8) Steve Rabin (ed.). Game AI Pro : collected wisdom of game AI professionals, 2013 (Ch. 6) Tyrrell, T.: Computational Mechanisms for Action Selection. Ph.D. Dissertation. Centre for Cognitive Science, University of Edinburgh (1993) |
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Last update: RNDr. Tomáš Holan, Ph.D. (04.01.2024)
Frontal teaching during lectures, solving practical problems during labs. |
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Last update: RNDr. Tomáš Holan, Ph.D. (04.01.2024)
Demonstrate an ability to apply techniques presented during lectures and demonstrated during practical lessons. |
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Last update: doc. RNDr. Tomáš Dvořák, CSc. (15.05.2024)
Lecture topics: 1. Taxonomy of artificial beings and their applications: learning simulations, video games, serious games, virtual storytelling, interactive drama, computational ethology. 2. Symbolic approaches to action selection: reactive planning, deliberative methods; if-then rules, finite-state machnies, behavioral trees, subsumption, Belief-Desire-Intention architecture, multi-layered architetures. 3. Connectionist approaches to action selection: free-flow hierarchies (Tyrrell), neural networks (Creatures, Black&White), approaches to agent learning. 4. Environment representation: affordances, smart objects, nav-mesh, way-points, sensory verisimilitude.
Tutorials are carried out in the virtual environment of the NOTA video game and include the following topics: 1. Introduction to Lua scripting language. 2. Introduction to behavior trees. 3. Decision patterns in behavior trees. 4. Controlling groups of bots using behavior trees. |