Course Unit Code | 460-4114/01 |
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Number of ECTS Credits Allocated | 4 ECTS credits |
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Type of Course Unit * | Choice-compulsory |
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Level of Course Unit * | Second Cycle |
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Year of Study * | Second Year |
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Semester when the Course Unit is delivered | Winter Semester |
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Mode of Delivery | Face-to-face |
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Language of Instruction | Czech |
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Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
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Name of Lecturer(s) | Personal ID | Name |
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| MEN059 | Mgr. Marek Menšík, Ph.D. |
Summary |
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In this course the students will learn how to design multi-agent systems. They should be able to design a system of autonomous agents who communicate by messaging in order to meet their individual as well as common goals. |
Learning Outcomes of the Course Unit |
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In this course the students will learn how to design multi-agent systems. They should be able to design a system of autonomous agents who communicate by messaging in order to meet their individual as well as common goals. |
Course Contents |
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Lectures:
1. Introduction to multiagent systems, agents' architecture, no central dispatcher
2. Behaviour vs. planning.
3. Reactive agents; Agents' learning and reasoning.
4. Deliberation, theory of BDI, IRMA (intelligent resource-bounded machine architecture), PRS (procedural reasoning system).
5. Agents' interactions, distributed artificial intelligence
6. Experiments: emergent behaviour of agents
7. Reactive communication with environment, standards of agents’ behavior.
8. Agents' cooperation, table method, negotiation, messaging.
9. Decentralized problem solving
10. Agents' Ontologies and knowledge bases
11. Communication in MAS, communication languages.
12. Indirect communication.
Exercisez:
1. Introduction to multiagent systems, agents' architecture, no central dispatcher
2. Behaviour vs. planning.
3. Reactive agents; Agents' learning and reasoning.
4. Deliberation, theory of BDI, IRMA (intelligent resource-bounded machine architecture), PRS (procedural reasoning system).
5. Agents' interactions, distributed artificial intelligence
6. Experiments: emergent behaviour of agents
7. Reactive communication with environment, standards of agents’ behavior.
8. Agents' cooperation, table method, negotiation, messaging.
9. Decentralized problem solving.
10. Ontologies and knowledge bases.
11. Indirect communication.
12. Languages for communication in multiagent systems. |
Recommended or Required Reading |
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Required Reading: |
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1. Wooldridge, M.:An Introduction to MultiAgent Systems, Wiley, 2009, ISBN:978-0470519462
2. Schwartz, H. M.: Multi-Agent Machine Learning: A Reinforcement Approach, Wiley, 2014, ISBN: 978-1118362082 |
1. Wooldridge, M.:An Introduction to MultiAgent Systems, Wiley, 2009, ISBN:978-0470519462
2. Schwartz, H. M.: Multi-Agent Machine Learning: A Reinforcement Approach, Wiley, 2014, ISBN: 978-1118362082
3. Kubík, A.:Inteligentní agenty, Computer Press, 2004, ISBN: 8025103234
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Recommended Reading: |
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1. Lewis, Zhang, Hengster-Movric, Das.:Cooperative Control of Multi-Agent Systems: Optimal and Adaptive Design Approaches (Communications and Control Engineering), Springer, 2014, 978-1447155737 |
1. Lewis, Zhang, Hengster-Movric, Das.:Cooperative Control of Multi-Agent Systems: Optimal and Adaptive Design Approaches (Communications and Control Engineering), Springer, 2014, 978-1447155737 |
Planned learning activities and teaching methods |
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Lectures, Seminars, Individual consultations, Tutorials, Project work |
Assesment methods and criteria |
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Task Title | Task Type | Maximum Number of Points (Act. for Subtasks) | Minimum Number of Points for Task Passing |
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Credit and Examination | Credit and Examination | 100 (100) | 51 |
Credit | Credit | 40 | 21 |
Examination | Examination | 60 | 30 |