Adversarial Search and Constraint Satisfaction

Welcome to Lesson 3 of Artificial Intelligence Concepts (PolyU COMP5511). In this session, we transition from single-agent pathfinding to Adversarial Search, where agents operate in competitive multi-agent environments. We also introduce Constraint Satisfaction Problems (CSPs), a paradigm where the goal is to find a state that satisfies a specific set of restrictions rather than a path.

Core Concepts

Adversarial Search: Focuses on algorithms like Minimax and Alpha-Beta Pruning to make rational decisions against an intelligent opponent.
Monte Carlo Tree Search (MCTS): Explores probabilistic decision-making, serving as the backbone for modern gaming AIs like AlphaGo.
Constraint Satisfaction: Models problems using Variables, Domains, and Constraints, solved via Backtracking and Local Search.

Complexity Analysis

In adversarial settings, the search space complexity is often defined by the game branching factor $b$ and depth $d$ , leading to the computational cost: $O (b^{d})$ This exponential growth necessitates efficient pruning strategies like Alpha-Beta Pruning.

Paradigm Shift Warning

Unlike standard search (e.g., A* or BFS) where the environment is static, Adversarial Search assumes the environment (the opponent) actively tries to minimize your success. In CSPs, the order of actions matters less than the validity of the final assignment.

Conceptual Pseudocode: Agent Types

# Adversarial Agent (Game Theory)
function Decide_Move(state):
    return Maximize_Utility(Predict_Opponent_Minimization(state))
# CSP Solver (Constraint Logic)
function Solve_CSP(variables, constraints):
    if All_Constraints_Satisfied(assignment):
        return assignment
    else:
        return Backtrack_Search(variables)

Course Roadmap

Transitioning from Search (Lesson 2) to Strategic Decision Making (Lesson 3).