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Lecture: Lecture: Intelligent Autonomous Agents and Cognitive Robotics - Details
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General information

Course name Lecture: Lecture: Intelligent Autonomous Agents and Cognitive Robotics
Subtitle Module: Intelligent Autonomous Agents and Cognitive Robotics
Course number 17973_W20
Semester WiSe 20/21
Current number of participants 118
Home institute Institut für Softwaresysteme (E-16)
Courses type Lecture in category Teaching
Type/Form Online Lecture
Pre-requisites Basic Mathematics
Performance record Written exam
ECTS points 6

Course location / Course dates

unspecified

Comment/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

- Artificial Intelligence: A Modern Approach (Third Edition), Stuart Russell, Peter Norvig, Prentice Hall, 2010, Chapters 2-5, 10-11, 13-17

- Probabilistic Robotics, Thrun, S., Burgard, W., Fox, D. MIT Press 2005
- Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations, Yoav Shoham, Kevin Leyton-Brown, Cambridge University Press, 2009

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