Foundations of Artificial Intelligence

Comprehensive overview of AI history from Alan Turing's work to modern developments, including key milestones, AI winters, philosophical debates about machine consciousness, and the Turing test.

Knowledge representation techniques including propositional logic, first-order logic, semantic networks, frames, ontologies, and reasoning systems for automated inference and decision-making.

Techniques for handling uncertainty in AI including probability theory, Bayesian networks, Markov models, fuzzy logic, and decision theory for reasoning under uncertainty and incomplete information.

Study of intelligent agent concepts including agent environments, agent types (simple reflex, model-based, goal-based, utility-based), multi-agent systems, and agent communication protocols.

Comprehensive coverage of problem-solving techniques including problem formulation, state space search, uninformed search algorithms, informed search algorithms, heuristic functions, and optimization algorithms.

Formal logic systems including propositional logic, predicate logic, modal logic, temporal logic, and automated theorem proving techniques used in AI for reasoning and verification.

AI planning techniques including classical planning, hierarchical planning, conditional planning, decision trees, game theory, and multi-agent planning for autonomous decision-making systems.

Expert system development including knowledge acquisition, rule-based reasoning, inference engines, knowledge engineering methodologies, and case-based reasoning systems for domain expertise automation.