K-INFO
HU
EN
Login
Subject » BMEVIMIM223
Older versions Download TAD (PDF)

Cooperation and Machine Learning Laboratory

Kooperáció és gépi tanulás labor
A tantárgyleírás hatályossága
Hatályosság kezdete:
2026. March 21.
Hatályosság vége:
—
Subject name (Hungarian, English)
Kooperáció és gépi tanulás labor
Cooperation and Machine Learning Laboratory
Subject code BMEVIMIM223
Subject type —
Training Level —
Course types and hours (weekly/semester)
Course type lecture tutorial laboratory
hours (weekly) 0 0 3
type (linked/independent) — — autonomous course
Assessment type félévközi érdemjegy
Credits 4
Subject coordinator
DR. Strausz György
position: egyetemi docens
Responsible department
Mesterséges Intelligencia és Rendszertervezés Tanszék
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. Simple text retrieval. The goal is to try several simpler text indexing and retrieval methods.

II. Domain modeling. The goal is to create a model of the domain necessary for the semantic search, and to familiarize with the Protégé ontology editing software tool.

III. Semantic information retrieval. The goal is to mend the results of the previous two steps: by using the model of the domain (the ontology) students extend the simple information retrieval with semantic capabilities.

IV. Game theoretic agents. Student experiment with several game theoretic models (games) by implementing them with simple JADE (Java Agent DEvelopment framework) agents. Cooperative, non-cooperative behaviors and equilibrium situations are examined.

V. Auctions and voting protocols. By using the standard message and protocol schemas, students build simple agent societies from simple JADE agents, and implement and manipulate more sophisticated auctions and voting protocols.

VI. Single-agent (centralized) planning. Students need to represent realistic planning domains and problems with an appropriate planning problem description language. The finished planning problem/domain representation is given as an input to a “black box” planner, which automatically computes the plan(s) solving the problem.

VII. Multi-agent (decentralized) planning. Students familiarize themselves with really distributed, multi-agent planning. The task of the students is to implement autonomous planning agents with BDI (Belief-Desire-Intention) architecture, which realize PRS-like (Procedural Reasoning System) reactive planning.

VIII. Static neural networks. Students construct several types of static neural networks to test the effect of different parameter settings in case of simpler classification tasks.

IX. Predicting time-series with dynamic networks. Students construct a system able to effectively predict the following element, or tens of elements in a ready-made data series by using dynamic networks (MLP, RBF, or SVM).

X. Bayesian learning. The goal is to examine domain model learning based on passive observations via Bayesian networks.

Theoretical knowledge related to the subject is presented within the Cooperation and Intelligence.

The laboratory is divided into thematic blocks, which are smaller projects, where students try to reach a given objective. Knowledge modeling and information retrieval block: where student implement more complicated, intelligent information search methods in a given domain. Cooperation block: where students implement agent societies participating in electronic auctions and voting via a game theoretic approach. Planning block: where students solve a planning and scheduling task via different AI planning methods. Learning block: where students experiment with static, dynamic, and Bayesian learning schemes in a given problem domain.  

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

Laboratory. Students participate in 10 laboratory (4 hours each) practices in a row.

Tanulástámogató anyagok

Online források
Stuart Russell and Peter Norvig: Artificial intelligence. a modern approach, 2nd edition, Prentice Hall, 2003; Notes for the laboratory work available on the web page of the subject (under preparation)

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  
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  
General rules
Requirements: Finishing every practice and deliver every associated report (the guidelines for making these reports are specified in the practice instructions accordingly). The final mark is based on the different report marks. Additional possibilities: Failed or absent practices can be repeated right after the last practice in the midterm. Maximally 2 practices can be repeated.  
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
None
Workload to complete the subject

No workload breakdown provided.

Validity of subject requirements
Requirements valid from:
—
Requirements valid until:
—
Curriculum placement

No curriculum placements recorded for this subject version.