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2. A fundamental AI problem-solving technique involving finding a suitable path from an initial state to a goal state.
6. The base game playing algorithm for two-person, zero-sum, perfect information games, which involves alternating between maximizing the computer's score and minimizing the opponent's best response.
7. Across 4: Descriptive knowledge, sometimes called "knowing-that," often formalized in facts and logical statements.
9. A type of AI system developed in the 1970s and 1980s that captured explicit human knowledge, typically in the form of IF-THEN rules, to solve specific, narrowly defined problems.
10. The practical complexity barrier in AI where the search complexity for problems (often $O(b^d)$) grows exponentially, quickly exceeding computing capabilities.
11. The logical principle ensuring that the truth of a conclusion is implicitly guaranteed by the truth of the premises.
12. The goal of AI that holds that a program can simulate intelligent behavior without making any claims to actual internal understanding or consciousness.

1. Imperative knowledge, or "knowing-how to do something," exemplified by instruction lists or procedures.
3. Rules of thumb or informal estimates used in informed search algorithms (like A* Search) to focus the search on promising paths.
4. The operational assessment for intelligent behavior where a human interrogator tries to determine if they are communicating with a machine or a person based purely on textual responses.
5. The abbreviation for the grand goal of building machines that possess general-purpose human-level intelligence.
8. The early 1960s program famous for achieving an "illusion of understanding" in dialogue using simple keyword patterns and canned responses.