ai-modern-approach

Artificial Intelligence: A Modern Approach

Apply this skill when the user is designing an intelligent agent, choosing between search/planning/reasoning techniques, or modeling a decision problem under uncertainty — use AIMA's agent-and-environment framing to pick the right class of solution instead of jumping straight to an algorithm.

Core concepts

Rational agent & PEAS — Frame any agent-design problem by its Performance measure (what defines success), Environment, Actuators (what it can do), and Sensors (what it can perceive). A "rational" agent is one that, given what it has perceived, acts to maximize expected performance — not one that's guaranteed correct or omniscient. When the user is designing an agent/bot/pipeline, start by making PEAS explicit; vague agent designs usually trace back to a vague performance measure.

Environment properties — Classify the environment before picking an approach: fully vs. partially observable, deterministic vs. stochastic, episodic vs. sequential, static vs. dynamic, discrete vs. continuous, single- vs. multi-agent. These properties directly determine which technique class is even appropriate (e.g. classical search assumes deterministic/fully-observable; POMDPs and RL exist because that assumption breaks).

Agent architectures — Match the agent's internal structure to what the task needs: simple reflex (condition-action rules, no memory), model-based reflex (tracks internal state to handle partial observability), goal-based (searches/plans toward an explicit goal), utility-based (optimizes a scalar utility, not just a boolean goal — needed when there are tradeoffs between outcomes), and learning agents (improve performance from experience). Don't default to the most complex architecture — pick the simplest one that satisfies the performance measure.

Uninformed vs. informed search — For deterministic, discrete problems reducible to search: uninformed search (BFS, DFS, uniform-cost) explores blindly and guarantees correctness but scales poorly; informed/heuristic search (greedy best-first, A*) uses a heuristic to guide exploration efficiently. A* is optimal only if the heuristic is admissible (never overestimates true cost) — and efficient if it's also consistent. When proposing a heuristic, check whether it's actually admissible, not just "reasonable-sounding."