Courses in Stud.IP

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Intelligent Autonomous Agents and Cognitive Robotics (VL)
Subtitle:
This course is part of the module: Intelligent Autonomous Agents and Cognitive Robotics
Semester:
WiSe 23/24
Course type:
Lecture
Course number:
lv341_w23
Lecturer:
Dipl. Informatiker Rainer Marrone
Description:
  • Definition of agents, rational behavior, goals, utilities, environment types
  • Adversarial agent cooperation: 
    Agents with complete access to the state(s) of the environment, games, Minimax algorithm, alpha-beta pruning, elements of chance
  • Uncertainty: 
    Motivation: agents with no direct access to the state(s) of the environment, probabilities, conditional probabilities, product rule, Bayes rule, full joint probability distribution, marginalization, summing out, answering queries, complexity, independence assumptions, naive Bayes, conditional independence assumptions
  • Bayesian networks: 
    Syntax and semantics of Bayesian networks, answering queries revised (inference by enumeration), typical-case complexity, pragmatics: reasoning from effect (that can be perceived by an agent) to cause (that cannot be directly perceived).
  • Probabilistic reasoning over time:
    Environmental state may change even without the agent performing actions, dynamic Bayesian networks, Markov assumption, transition model, sensor model, inference problems: filtering, prediction, smoothing, most-likely explanation, special cases: hidden Markov models, Kalman filters, Exact inferences and approximations
  • Decision making under uncertainty:
    Simple decisions: utility theory, multivariate utility functions, dominance, decision networks, value of informatio
    Complex decisions: sequential decision problems, value iteration, policy iteration, MDPs
    Decision-theoretic agents: POMDPs, reduction to multidimensional continuous MDPs, dynamic decision networks
  • Simultaneous Localization and Mapping
  • Planning
  • Game theory (Golden Balls: Split or Share) 
    Decisions with multiple agents, Nash equilibrium, Bayes-Nash equilibrium
  • Social Choice 
    Voting protocols, preferences, paradoxes, Arrow's Theorem,
  • Mechanism Design 
    Fundamentals, dominant strategy implementation, Revelation Principle, Gibbard-Satterthwaite Impossibility Theorem, Direct mechanisms, incentive compatibility, strategy-proofness, Vickrey-Groves-Clarke mechanisms, expected externality mechanisms, participation constraints, individual rationality, budget balancedness, bilateral trade, Myerson-Satterthwaite Theorem
Performance accreditation:
655 - Intelligent Autonomous Agents and Cognitive Robotics<ul><li>655 - Intelligent Autonomous Agents and Cognitive Robotics: Klausur schriftlich</li></ul>
ECTS credit points:
6
Stud.IP informationen about this course:
Home institute: Institut für Softwaresysteme (E-16)
Registered participants in Stud.IP: 107
former semester
link to course in Stud.IP Studip_icon
Intelligent Autonomous Agents and Cognitive Robotics (VL)
Subtitle:
This course is part of the module: Intelligent Autonomous Agents and Cognitive Robotics
Semester:
WiSe 23/24
Course type:
Lecture
Course number:
lv341_w23
Lecturer:
Dipl. Informatiker Rainer Marrone
Description:
  • Definition of agents, rational behavior, goals, utilities, environment types
  • Adversarial agent cooperation: 
    Agents with complete access to the state(s) of the environment, games, Minimax algorithm, alpha-beta pruning, elements of chance
  • Uncertainty: 
    Motivation: agents with no direct access to the state(s) of the environment, probabilities, conditional probabilities, product rule, Bayes rule, full joint probability distribution, marginalization, summing out, answering queries, complexity, independence assumptions, naive Bayes, conditional independence assumptions
  • Bayesian networks: 
    Syntax and semantics of Bayesian networks, answering queries revised (inference by enumeration), typical-case complexity, pragmatics: reasoning from effect (that can be perceived by an agent) to cause (that cannot be directly perceived).
  • Probabilistic reasoning over time:
    Environmental state may change even without the agent performing actions, dynamic Bayesian networks, Markov assumption, transition model, sensor model, inference problems: filtering, prediction, smoothing, most-likely explanation, special cases: hidden Markov models, Kalman filters, Exact inferences and approximations
  • Decision making under uncertainty:
    Simple decisions: utility theory, multivariate utility functions, dominance, decision networks, value of informatio
    Complex decisions: sequential decision problems, value iteration, policy iteration, MDPs
    Decision-theoretic agents: POMDPs, reduction to multidimensional continuous MDPs, dynamic decision networks
  • Simultaneous Localization and Mapping
  • Planning
  • Game theory (Golden Balls: Split or Share) 
    Decisions with multiple agents, Nash equilibrium, Bayes-Nash equilibrium
  • Social Choice 
    Voting protocols, preferences, paradoxes, Arrow's Theorem,
  • Mechanism Design 
    Fundamentals, dominant strategy implementation, Revelation Principle, Gibbard-Satterthwaite Impossibility Theorem, Direct mechanisms, incentive compatibility, strategy-proofness, Vickrey-Groves-Clarke mechanisms, expected externality mechanisms, participation constraints, individual rationality, budget balancedness, bilateral trade, Myerson-Satterthwaite Theorem
Performance accreditation:
655 - Intelligent Autonomous Agents and Cognitive Robotics<ul><li>655 - Intelligent Autonomous Agents and Cognitive Robotics: Klausur schriftlich</li></ul>
ECTS credit points:
6
Stud.IP informationen about this course:
Home institute: Institut für Softwaresysteme (E-16)
Registered participants in Stud.IP: 107

Courses

For information on courses and modules, please refer to the current course catalogue and module manual of your degree programme.

Module / Course Period ECTS Credit Points
Module: Electrical Power Systems I: Introduction to Electrical Power Systems WiSe 6
Module: Electrical Power Systems II: Operation and Information Systems of Electrical Power Grids WiSe 6
Module: Electrical Power Systems III: Dynamics and Stability of Electrical Power Systems SuSe 6
Module: Electrical Engineering II: Alternating Current Networks and Basic Devices SuSe 6
Module: Electrical Engineering Project Laboratory SuSe 6
Module: Process Measurement Engineering SuSe 4
Module: Smart Grid Technologies WiSe, SuSe 6

Course: Seminar on Electromagnetic Compatibility and Electrical Power Systems

Further Information

WiSe, SuSe 2

SuSe: Summer Semester
WiSe: Winter Semester