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Computational Intelligence

The course introduces various computational methods and data processing that can be labeled "artificial intelligence".

In more details students will:

• gain expertise on top-down and bottom-up methods where solutions are searched or built through trial-and-error
• acquire knowledge on approximate optimization techniques and exploit the relationship between optimization and learning
• understand the concepts of path-, local-, and policy-search
• learn how to tackle problems involving highly-structured, uncertain (possible), and imprecise (fuzzy) information

The course also encompasses general multi-agent systems and a range of algorithms inspired by natural systems, such as genetic algorithms, genetic programming, and swarm optimization. The lectures will cover theoretical foundations, algorithm design, implementation, and applications to real-world problems.

Introduction

• What is "Computational Intelligence"? (included: what is "Artificial Intelligence"? Weak AI vs. Strong AI; the Turing Test; ...)
• Symbolic vs. Sub-symbolic intelligence
• Solving problems by searching; trial n'error vs. learning vs. evolution
• Metaheuristics (exact vs. approximate, ad-hoc heuristics)
• Evolutionary Computation (bio-inspired methodologies, natural selection)

Single-State Methods

• Hill-climbing, simulated annealing, iterated local search, variable neighborhood search
• Simple Evolution Strategies: (1+1), (1+λ) and (1,λ)

Population Methods

• Unified approach to Evolutionary Algorithms
• Parameter optimization (Evolution Strategies, Differential Evolution)
• Symbolic regression (Genetic Programming)
• Swarm intelligence (Ant Colony Optimization, Particle Swarm Optimization)
• Memetic Algorithms (hybridization)
• Model fitting (Estimation of Distribution Algorithm)
• Multi-objective optimization

Representation problems; genotype space and operators

• Knowledge representation
• Trivial (bit strings, integer, real numbers); Permutations; Graphs
• Fuzzification

Policy optimization

• Reinforcement learning, Q-Learning (not included: Deep Q-Learning)
• Rule-based systems and Learning Classifier Systems

Multi agent systems

• Artificial Immune System and Learning Classifier System
• Simple agents
• Learning agents
• Games (Adversarial Search)