CIS 5603. Artificial Intelligence

Searching State Space

1. Problem solving as graph search

One of the earliest AI approaches is to see intelligence as problem-solving capability, and to specify problem-solving as state-space search, that is, by selecting applicable actions, changing the initial state into a goal state, step by step.

Example: 8-puzzle

A generic searching algorithm: a sequence of selections

Direction of search: forward (data-driven), backward (goal-driven), or bidirectional.

Information about the selections: Exhaustive search can be considered as a general-purpose solution if the graph is finite and resource expense is ignored, though in reality resource is limited and response time matters. Therefore, combinatorial (exponential) explosion is a hard problem in AI, though the issue is not merely on computational complexity or hardware.

2. Heuristic search

AI research focuses on partially informed search, such as heuristic search, or "Best-first" search, where the "Frontier" is a priority queue with a heuristic function to decide the search order of nodes, as in A* algorithm.

Example: General Problem Solver (GPS) using means-ends analysis to get heuristics.

Newell-Simon76: "A physical symbol system has the necessary and sufficient means for general intelligent action", "physical symbol systems solve problems by means of heuristic search".

A general principle: under bounded rationality, an agent should seek a satisfactory solution rather than an optimal one. This principle is acknowledged as widely applicable.

3. Game playing

Two-player zero-sum games are often handled as state-space search using minimax algorithm, which find the most "rational" moves for both palyers.

Example: Tic-tac-toe.

To improve the efficiency of searching, various ideas have been tried:

4. Analysis

When deciding whether to use heuristic search to solve a problem, the major questions to be asked are: In general, a problem-solving process involves a sequence of decisions or choices, even though not necessarily among enumerable alternatives at well-defined states. AI problems are often characterized by partially-informed decisions in changing environments, where traditional theories (on computability, computational complexity, decision making, gaming playing, operations research, etc.) are no longer fully applicable.

In particular, dealing with real-time requirement is even beyond the restriction of "bounded rationality". One possible approach is parallel search with dynamic resource allocation basing on learned priority.