At the end of this module, students should be able to:

identify, contrast and apply to simple examples the major search techniques that have been developed for problem-solving in AI;

distinguish the characteristics, and advantages and disadvantages, of the major knowledge representation paradigms that have been used in AI, such as production rules, semantic networks, propositional logic and first-order logic;

solve simple knowledge-based problems using the AI representations studied;

identify or describe approaches used to solve planning problems in AI and apply these to simple examples;

identify or describe the major approaches to learning in AI and apply these to simple examples;

identify or describe some of the major applications of AI;

understand and write Prolog code to solve simple knowledge-based problems.

• Introduction (3 lectures): What is Artificial Intelligence? Characterisation of AI; historical overview; intelligent agents; agents’ environments; applications of AI; current state-of-the-art.

• Problem-Solving Through Search (7 lectures): Problem formulation; uninformed search strategies; informed search strategies; search in complex environments; adversarial search.

• Knowledge Representation (4 lectures): Characterisation plus advantages and disadvantages of rule-based systems, semantic networks, ontologies and logics.  Example applications of different knowledge representation schemes.
  

• Logic (4 lectures): Reasoning in propositional and first-order logic.

• Planning (3 lectures): Representing planning problems; classical planning approaches, including search, heuristics and satisfiability; planning in complex environments.

• Learning (4 lectures): Different forms of learning; logic and learning; reinforcement learning.

• Basics of Prolog (5 lectures): Facts, rules and queries; recursion; lists; negation as failure.

Lecture -

3 per week during semester

Laboratory Work -