Artificial and Computational Intelligence
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Artificial and Computational Intelligence studies how agents perceive, reason, search, optimise, learn and act in complex environments.
Core Learning Pages
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| # | Topic |
|---|
| 1 | Artificial Intelligence |
| 2 | Intelligent Agents and Environments |
| 3 | Problem Solving and Uninformed Search |
| 4 | Informed Search and Heuristic Design |
| 5 | Local Search and Optimisation |
| 6 | Evolutionary and Population-Based Search |
| 7 | Game Playing and Adversarial Search |
| 8 | Knowledge Representation and Propositional Logic |
Modular Structure
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1. Introduction
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- Artificial Intelligence foundations
- Overview of modern AI
- AI application domains
2. Introduction to Intelligent Agents
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- Notion of agents and environments
- Rationality
- Nature of environments
- Structure of agents
3. Problem-Solving Agents Using Search
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- Problem formulation
- Uninformed and informed search algorithms
- Heuristics
- Greedy Best-First Search
- A* Search and optimality of A*
- Heuristic accuracy and algorithm performance
- Admissible heuristics from relaxed problems
- Pattern databases and experience
- Learning heuristics
- Local search and optimisation
- Hill Climbing
- Local Beam Search
- Genetic Algorithms
- Ant Colony Optimisation
- Neural Architecture Search
- Neuroevolution
4. Game Playing
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- Minimax Algorithm
- Alpha-Beta Pruning
- Monte Carlo Tree Search
- Stochastic Games
5. Knowledge Representation Using Logic
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- Propositional and Predicate Logic
- TT-Entail and theorem proving
- Logic representation of intelligent agents
- Proof by resolution
- DPLL Algorithm
- Agents based on Propositional Logic
- Unification
- Forward Chaining
- Backward Chaining
- Resolution
6. Multi-Agent Decision Making
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- Properties of multi-agent environments
- Multi-agent planning
- Non-Cooperative Game Theory
- Cooperative Game Theory
- Collective decision making
7. Probabilistic Representation and Reasoning
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- Representing knowledge in uncertain domains
- Semantics of Bayesian Networks
- Exact inference in Bayesian Networks
- Approximate inference in Bayesian Networks
8. Probabilistic Reasoning Over Time
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- Time and uncertainty
- Inference in temporal models
- Hidden Markov Models
- Learning HMMs
- Dynamic Bayesian Networks
9. Ethics in AI
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- Explainable AI
- Logically Explained Networks
- Explainable Bayesian Networks
Experiments
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| # | Experiment |
|---|
| 1 | Implement Uninformed Search Algorithms such as BFS and DFS |
| 2 | Implement the A* Algorithm for Informed Search |
| 3 | Implement Local Search Techniques using a Genetic Algorithm |
| 4 | Implement the Minimax Algorithm for Adversarial Search in game playing |
| 5 | Represent knowledge using logic and perform reasoning using Prolog |
| 6 | Experiment with Bayesian Networks and exact inference |
| 7 | Experiment with the application of a Hidden Markov Model in Natural Language Processing |
- Programming languages: Python, Prolog
- Tools and libraries: Jupyter, NumPy, SciPy, Pandas, pgmpy, NLTK
- Environments: Google Colab, SWI-Prolog Online
Book References
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Primary Textbook
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- Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 4th Edition, Pearson Education, 2020.
Reference Books
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- Ryszard S. Michalski, Jaime G. Carbonell and Tom M. Mitchell, Machine Learning: An Artificial Intelligence Approach, Elsevier, 2014.
- Dan W. Patterson, Introduction to AI and Expert Systems, Prentice Hall of India, New Delhi, 2010.
- Elaine Rich and Kevin Knight, Artificial Intelligence, 2nd Edition, Tata McGraw Hill Publishing Company, New Delhi, 2003.
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