AI history of games research: Arthur Samuel is most closely associated with developing a pioneering self-learning program that played which game?

Introduction / Context:
Games have long served as testbeds for AI because they offer clear rules, measurable outcomes, and vast search spaces. Arthur Samuel's work is a landmark: he engineered one of the first programs that improved its play over time by learning from experience—an early demonstration of machine learning in action.

Given Data / Assumptions:

• We must identify the specific game tied to Samuel's self-learning program.
• Chess is a common distractor due to other AI milestones, but the earliest learning system here was not chess.
• Non-board-sport options are irrelevant in this historical context.

Concept / Approach:
Samuel's program learned evaluation functions and improved move selection through mechanisms such as rote learning and self-play. The domain was checkers (draughts), chosen for tractability and the ability to encode board states and transitions efficiently for mid-20th-century hardware constraints.

Step-by-Step Solution:

Verification / Alternative check:
AI histories consistently credit Samuel's checkers program as a pioneering example of machine learning and game-playing AI, reinforcing the answer.

Why Other Options Are Wrong:

• Chess: a major AI domain, but not Samuel's famed learning program.
• Cricket/Football: unrelated to early computational game-playing research.
• None: incorrect because checkers is the established association.

Common Pitfalls:
Assuming early AI game work was primarily chess; overlooking checkers' key role in demonstrating learning from self-play.

Final Answer:
checkers