AI for Games and Puzzles

Competitive mind games, in particular Chess, have long been recognised as proving grounds for Artificial
Intelligence techniques, partly because they provide environments in which simple and thus easily formalised
rules can lead to extremely complex skilled behaviours which are most challenging to simulate automatically.
Even though the target of world-champion strength Chess programs has now been achieved, there are other
deterministic games - notably Go - which are not likely amenable to the same brute-force techniques that have
succeeded in Chess. Similarly, some puzzles are so complex that algorithmic solution is not practical, and
heuristic means of solving them is necessary. Some games with an element of chance, such as Poker and
BackGammon, cannot be solved algorithmically either. Such games and puzzles require novel techniques,
including learning from experience and (in games) modelling the reasoning of opponents, that bear more closely
upon practical real-world problems than the idealisation of Chess does. The module covers elementary game
theory, and presents a variety of heuristic game-tree search techniques. It proceeds to treat issues of
pattern matching, reasoning using chunking, and machine learning, all in the realm of game playing and puzzle
solving