Subject » BMEVIMIM306
Embedded Intelligent Systems Laboratory
Beágyazott intelligens rendszerek laboratórium
A tantárgyleírás hatályossága
Hatályosság kezdete:
2026. March 21.
Hatályosság vége:
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| Subject name (Hungarian, English) |
Beágyazott intelligens rendszerek laboratórium
Embedded Intelligent Systems Laboratory
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| Subject code | BMEVIMIM306 | ||||||||||||
| Subject type | — | ||||||||||||
| Training Level | — | ||||||||||||
| Course types and hours (weekly/semester) |
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| Assessment type | félévközi érdemjegy | ||||||||||||
| Credits | 4 | ||||||||||||
| Subject coordinator |
Dr. Pataki Béla József
position: adjunktus
contact:
pataki.bela@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. Sensor fusion. Students practice with fusing multi-sensor information from different motes and other stationary sensors.VIII. Building
II. Human-agent communication: controlled natural language dialogue. During this practice students extend motes and agents with natural language processing capabilities in order to communicate with humans in a controlled fashion in an intelligent space (e.g. intelligent office).
III. Human-agent communication: emotional models. During this practice student extend motes and agents with the emotional models of the humans in the intelligent space (e.g. intelligent office) in order to cooperate with them more effectively.
IV. Planning in mote-environment. Students create planning operators abstracting the movement and actions of mobile motes, and then represent concrete mote-tasks in a planning problem description language to generate the solution plans. The correctness of the plans is test by their execution.
V. Expert agents. During this practice students control motes and other actuators with agents, which diagnose the actual scenario with expert systems (based on sensory input).
VI. Constraint logic programming. During this practice students control motes and other actuators with agents, which make their decisions with a constraint logic solver engine (based on sensory input).
VII. Fuzzy production systems. During this practice students control motes and other actuators with agents, which make their decisions with fuzzy production systems (based on sensory input).
IX. Evolutionary methods. Students solve several hard optimization problems with evolutionary/genetic methods, and examine the evolutionary processes in dedicated software environment. They experiment with modifying the genetic coding and genetic operators. The created solutions (parameter vectors, controlling mechanisms) are tested with motes.
X. Emergent and swarm intelligence. Students implement and analyze several cooperative social models (bees, ants, etc) in agent societies and appropriately made physical test environments.
Theoretical knowledge related to the subject is presented within the Embedded Intelligent Systems.
During the course of the laboratory students are introduced to the practice of embedded intelligent systems. The practices are particularly about ambient intelligent systems. Students practice with sensor fusion; human-agent communication; expert, logical, planning, fuzzy, and decision network based decision systems; and with evolutionary, emergent, and swarm intelligence.
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 practices in a row.
Tanulástámogató anyagok
Online források
Stuart Russell and Peter Norvig: Artificial intelligence. a modern approach, 2nd edition, Prentice Hall, 2003
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. There is no repetition in the examination period.
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:
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Requirements valid until:
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Curriculum placement
No curriculum placements recorded for this subject version.