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Adversarial search & Multi agent environments - Coggle Diagram
Adversarial search & Multi agent environments
AND-OR search trees
Structure
Building an AND/OR Search Tree
Search Algorithms for AND/OR Search Tree
Multiplayer Games
Replacing single values with vectors
Handling games with more then two players
Steering behaviours
Emergence
Introduction to Adversarial Search
Definition Purpose of adversarial search
Applications in games and strategic decision-making
Minimax Algorithm
Overview of the minimax algorithm for adversarial search
Minimax tree and its construction.
Evaluation functions and terminal states
Minimax decision-making process
Alpha-Beta Pruning
Introduction to alpha-beta pruning
Reducing the number of evaluated states in the minimax tree
Alpha and beta values and their role in pruning
Improved efficiency of alpha-beta pruning compared to minimax.
Enhancements to Adversarial Search
Iterative deepening for better time management
Transposition tables for caching and reducing redundant computations
Move ordering techniques to improve alpha-beta pruning
Introduction to Multi-Agent Environments
Definition and characteristics
Importance and applications in AI, robotics, and game theory
Types of Multi-Agent Environments
Cooperative environments where agents work together towards shared goals
Competitive environments where agents aim to outperform each other
Mixed environments combining elements of cooperation and competition
Communication and Coordination in Multi-Agent Environments
Importance of communication between agents.
Methods of communication: explicit messages, implicit signaling, and observation
Coordination mechanisms: task allocation, negotiation, and agreement
Game Theory and Multi-Agent Environments
Introduction to game theory and its relevance to multi-agent environments
Cooperative and non-cooperative game models
Prisoner’s dilemma
Nash equilibrium and its applications
Optimising agent performance
Strategic decision making