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Adversarial search & Multiagent environments - Coggle Diagram
Adversarial search & Multiagent environments
Adversarial search
What if there’s another vacuum cleaner out there, trying to suck up dirt too?
Multi-agent environments
For AI, convenient models are deterministic, turn-taking and two-player
Solving games
.
Challenging
Chess search tree, av. 50 moves each, 35 branches per node = 10 to power 154 nodes!
Calculating optimal decision infeasible
Some decision must be made
Max and Min
2 players - Max and Min
Formal description of game:
Minimax algorithm
Normal search - actions lead to goal
Adversarial search - Max has to consider Min’s moves
It’s just like AND-OR search tree
Multi-agent environments
Planning - cooperation and coordination
:
Evolution
Conventions can be evolved behaviours
Emergence
Patterns of behaviour
Individuals follow simple rules
web link
https://www.javatpoint.com/ai-adversarial-search
https://www.javatpoint.com/mini-max-algorithm-in-ai
https://investoedia.com/tearm/g/gametheory.asp