Subject » BMEVIMIM135
Cooperation and Intelligence
Kooperáció és intelligencia
A tantárgyleírás hatályossága
Hatályosság kezdete:
2026. March 21.
Hatályosság vége:
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| Subject name (Hungarian, English) |
Kooperáció és intelligencia
Cooperation and Intelligence
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| Subject code | BMEVIMIM135 | ||||||||||||
| Subject type | — | ||||||||||||
| Training Level | — | ||||||||||||
| Course types and hours (weekly/semester) |
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| Assessment type | vizsga | ||||||||||||
| Credits | 4 | ||||||||||||
| Subject coordinator |
Dr. Dobrowiecki Tadeusz Pawel
position: egyetemi docens
contact:
dobrowiecki.tadeusz@vik.bme.hu
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| Responsible department |
Mesterséges Intelligencia és Rendszertervezés Tanszék
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| Faculty | Villamosmérnöki és Informatikai Kar | ||||||||||||
| Subject website | — | ||||||||||||
| Primary curriculum type | — | ||||||||||||
| Direct prerequisites – Strong prerequisite | none | ||||||||||||
| Direct prerequisites – Weak prerequisite | none | ||||||||||||
| Direct prerequisites – Parallel prerequisite | none | ||||||||||||
| Direct prerequisites – Milestone prerequisite | none | ||||||||||||
| Direct prerequisites – Exclusion | none |
Objectives
Programme
I. Agent basics: Basic topics in communication and cooperation. Agents. Intelligent agents. Multi-agent systems.
II. Logical basis of agent communication and beyond: Cooperation in agent systems based on modal logical models (Belief-Desire-Intention). BDI model based agent modeling and communication languages - AgentSpeak(L) and Jason platform.
III. Learning in multi-agent systems: Special problems and opportunities of learning in multi-agent systems. Learning in cooperative organizations. Learning in competitive organizations. Learning in hierarchical organizations.
IV. Conceptual systems and cooperation: Basics of ontological systems. The role of ontologies. Ontologies in agent communication, in open systems. Ontologies in agent-human interactions.
V. Game theoretical models: Cooperation and Game Theory. Utility Theory: preference, utility, transitivity. Non-cooperative games. Rationality, common knowledge, perfectness, completeness, players, pure and mixed strategies, Nash-equilibrium, types, incomplete information (Bayesian) games, cooperative games.
VI. Competition in open systems: Cooperative conflict resolution. Auction- and Voting Theory. Single/multi-item, first/second-price, and sealed-bid auctions. Mechanism design, Social choice functions, dominant- and Nash-implementation, Vickrey-Clarke-Groves mechanisms, Revelation principle.
VII. Planned activities: Multi-agent planning. Basics. Planning in open systems with incomplete information. Embedding plans into agent modeling languages. Planning and communication planning.
Practical knowledge related to the subject is presented within the Cooperation and machine learning Lab.
The aim of the subjects is multiple level analysis of cooperation, from basic informational structures to the solutions used in intelligent systems. During the semester, starting from low-level infrastructural layers, then progressing toward higher-level layers calling for intelligent behavior, we deal with the following topics: distributed machine learning, game theoretical problems, voting systems, ontologies, languages, communication and cooperation, distributed problem solving via cooperative communication. We expect that the students successfully fulfilling the requirements of the subject will have a clear view of the potential of the cooperative system solutions and of the spectrum of the cooperative technologies, will be able to design and analyze cooperative system models in practical applications, will gain working knowledge of the cooperative game theoretical solutions and high-level AI methods used in cooperative intelligent systems.
Learning outcomes
Ez a tantárgy a KKK rendeletben meghatározott, következő kompetenciák fejlesztését szolgálja:
Knowledge
No learning outcomes recorded.
Skills
No learning outcomes recorded.
Attitudes
No learning outcomes recorded.
Autonomy and responsibility
No learning outcomes recorded.
Oktatási módszertan
Theoretical part of the curriculum is taught during the lectures. Practical experimentation is supported by the home work, software demonstrations during the lectures, and by the related laboratory practice.
Tanulástámogató anyagok
Online források
Lecture notes made available at the home page of the subject, suggested electronic literature and additional information, and a web link collection. Wooldridge, M., An Introduction to Multi-agent Systems, J. Wiley, 2002 Rafael H. Bordini, Jomi Fred Hübner, Michael Wooldridge, Programming Multi-Agent Systems in AgentSpeak using Jason, J. Wiley, 2007; Stuart Russell and Peter Norvig: Artificial intelligence. The modern approach, 2nd edition, Prentice Hall, 2001 T. Mitchell: Machine Learning, McGraw-Hill, 1997. F. L. Bellifemine, G. Caire, D. Greenwood: Developing Multi Agent Systems with JADE, Wiley, 2007
Recommended preliminary knowledge for completing the subject
Knowledge type competencies
(azon előzetes ismeretek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
Artificial intelligence, Programming techniques (Java), Cooperative and learning systems
Skill type competencies
(azon előzetes képességek és készségek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
nincs
Recommended (non-compulsory) preliminary competencies
(azon ajánlott (nem kötelező) előzetesen megszerzendő kompetenciák összessége, amelyek jelentősen hozzájárulnak a tantárgy eredményes teljesítéséhez)
Artificial intelligence, Programming techniques (Java), Cooperative and learning systems
General rules
Requirements:
a. During the semester:
· 8 small home assignments to be handed out every week and a large home assignement. Reports with the solutions are due on the 13th week of the semester. The joint presentation of the solutions and the qualification (assuming the reports are ready) is on the 14th week of the semester. The assignment brings max. 40 points, the required minimum is 40%. Small assignments bring 0 … 5 points (bad, good, very good). The required minimum is 5 points.
b. During the examination period: oral exam. Students qualify for the final exam with minimal level achievements in home assignments (40 %).
c. Qualification: The final mark is based on the number of points collected from the home work, and the final exam.
Additional possibilities:
Failed home assignments can be handed in until the end of the supplementary week.
Assessment methods
In-term assessments
No detailed assessments provided.
Weight of in-term assessments
No weights provided.
Exam-period assessments
No detailed assessments provided.
Weight of exam elements
No weights provided.
Grade calculation
No grade thresholds provided.
Attendance requirements
No attendance requirements provided.
Rules for retake and resubmission
Not provided.
Short description
Not provided.
Detailed description
Not provided.
Recommended courses
Artificial intelligence (BSc), Cooperative and learning systems (BSc).
Workload to complete the subject
No workload breakdown provided.
Validity of subject requirements
Requirements valid from:
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Requirements valid until:
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Curriculum placement
No curriculum placements recorded for this subject version.