Administrative information
| Title |
Decision theory |
 |
| Duration |
60 |
| Module |
A |
| Lesson Type |
Lecture |
| Focus |
Technical - Foundations of AI |
| Topic |
Foundations of AI |
Keywords
consequentialism, subjectivism, probability theory, utility theory, decision theory, optimal decision, bounded rationality, satisficing, cognitive bias, effective altruism, off-switch game, sequential decisions, value of information, multi-armed bandit, exploration-exploitation dilemma,
Learning Goals
- The learner gets acquainted with non-quantitative and quantitative approaches to ethics
- students can define and calculate optimal decisions using univariate distributions and utility/loss functions
- students can know the elements of decision networks: stochastic/action/utility nodes
- students can know and apply Bayes' rule (multivariate)
- students can construct a Naive Bayesian Network (NBN) model
- students can expand NBN to an NBN-based decision network
- students can calculate the value of perfect information
Expected Preparation
Learning Events to be Completed Before
Obligatory for Students
- Probability distribution, conditional probability, expected value (e.g from AIMA4e or wikipedia)
- Influence diagram
Optional for Students
- Artificial Intelligence: A Modern Approach, 4th Global ed. by Stuart Russell and Peter Norvig, Pearson (AIMA4e):ch12-18
References and background for students:
Lesson Materials
The materials of this learning event are available under CC BY-NC-SA 4.0.
Instructions for Teachers
- Adapt the basic triple node decision net
- Find the best and worst decisions
- Adapt the naive Bayes net
- Find the most valuable evidence (feature)
- During in-class activities, ask students to guess optimal decisions
Outline/time schedule
| Duration |
Description |
Concepts |
Activity |
Material |
| 5 |
Sources of uncertainty and Interpretations of probability |
uncertainty |
|
|
| 5 |
Bernoulli and multinomial distributions |
univariate distributions |
|
|
| 5 |
Axioms of probability theory (additivity) |
probability theory |
|
|
| 5 |
Elements and graphical notation of a single-step decision problem: the decision network of stochastic→utility/loss←action nodes |
decision problem |
|
|
| 5 |
Utility and loss functions, common loss functions and matrices |
preferences |
|
|
| 5 |
Expected value, the maximum expected utility principle |
optimal decision |
|
|
| 5 |
Conditional probability and Bayes' theorem (for two variables and with condition) |
conditional probability |
|
|
| 5 |
Independence and conditional independence |
independence |
|
|
| 5 |
Example of a Naive Bayesian network |
Naive Bayes net |
|
|
| 5 |
Example of decision network based on a Naive Bayesian network |
|
|
|
| 5 |
Posterior inference and selection of optimal decision |
posterior inference |
|
|
| 5 |
Between evidence inference and calculation of the value of information |
value of information |
|
|
More information
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
Acknowledgements
 |
The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.
The materials of this learning event are available under CC BY-NC-SA 4.0
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
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