an introduction to artificial life lecture 4b: informed search and exploration ramin halavati...
TRANSCRIPT
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An Introduction to Artificial Life
Lecture 4b: Informed Search and ExplorationRamin Halavati ([email protected])
In which we see how information about the state space can prevent algorithms from blundering about the dark.
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Local search algorithms• Some times the path to goal matters…
– Shortest route to a city.– The solution to 8-Puzzle.– Robot’s route in a building.– A Check-Mate
• And some times, not– 8 Queens.– Job-Shop Scheduling– Automatic program generation– Check-Mate in Barareh!
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Local search algorithms• When path doesn’t matter…
• State space = set of "complete" configurations
• Find configuration satisfying constraints
• Keep a single "current" state or a fixed number of independent current states, try to improve it or them.
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Objective Landscape
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Hill-Climbing Search• "Like climbing Everest in thick fog with
amnesia"
• Greedy Local Search
•
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Hill-Climbing Search: 8-Queens
• h = number of pairs of queens that are attacking each other, either directly or indirectly
• h = 17 for the above state
•
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Hill Climbing Problems• Local Maxima/Minima
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Hill Climbing Problems
• A local minimum with h = 1•
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Hill Climbing Problems• Ridges
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Hill Climbing Problems
• Plateaux– A state whose all neighbors have similar
fitness.
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Hill Climbing, Now What?• 8-Queens, random examples:
– 86% Failure, 14% Success.– In average, 4 moves.
– State Space: 88 > 17,000,000
• Solution 1: Random Restart:– Restart from a random point if failed.
• Almost 7 tries for 8-queens.• 3,000,000 queens in less than 1 minute.
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Hill Climbing, Now What?• Solution 2:
– Sideway moves for plateaux. / Limited.
• Solution 3:– Stochastic Hill Climbing, random selection
among up hills.• Slower convergence, sometimes better solutions.
• Solution 4:– First Choice Hill Climbing.
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Practical State Spaces
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Simulated Annealing Search• Hill Climbing:
– Just move to a better state. – Efficient, but can stuck in local maxima
• Random Walk:– Move to a random neighbor.– Complete, but extremely inefficient.
• Idea: Escape local maxima by allowing some "bad" moves but gradually decrease their frequency.
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Simulated Annealing Search
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Simulated Annealing Search• One can prove: If T decreases slowly enough,
then simulated annealing search will find a global optimum with probability approaching 1
• Widely used in VLSI layout, airline scheduling, etc.
•
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Local Beam Search• Keep track of k states rather than just one
• Start with k randomly generated states
• At each iteration, all the successors of all k states are generated
• If any one is a goal state, stop; else select the k best successors from the complete list and repeat.
•••
•
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Local Beam Search• Looks similar to Parallel Random Start Hill
Climbers, but it’s not.
• Stochastic Beam Search
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Genetic Algorithms• Beagle Voyage
• Animals adopt to
environmental.
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Genetic Algorithms• Natural Evolution:
– Given these requirements:1. A creature whose features affects his reproduction
rate.2. Offspring features are very much, but not exactly
similar to the parent(s).3. There is a competition on resources.
– We will have• Gradual Progress
– The Blind Watchmaker - DawkinsThe Blind Watchmaker - Dawkins
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Genetic algorithms• Start with k randomly generated states
(population)
• A successor state is generated by combining two parent states
• Produce the next generation of states by selection, crossover, and mutation.
• Evaluation function (fitness function). Higher values for better states.– Selection of Higher Fitnesses
••
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Genetic algorithms• Cross Over:
– To select some part of the solution (state) from one person and the rest from another.
• Mutation:– To change a small part of one solution with a small probability.
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Genetic algorithms
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L.S. in Continues Spaces• Infinite number of successor states.
– To select three best locations for airports.• (x1,y1) , (x2,y2) , (x3,y3)
• Approach 1:– To discretize
• Just change them by ±
• Approach 2:– To compute gradient!
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Online Search / Unknown Env.
• Offline Search– Generate as many nodes as you wish, in any
order.
• Online Search– Interleave computation and action
• Dynamic Domains• Stochastic Domains.• Unknown Domains.
– Exploration
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Online Search Problems• You can see the successors just by applying an
action.
• You may have different results from repeating a similar action at a certain state.
• You just see one step ahead.G
S
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Algorithms for Online Search• Nothing General Enough
• Limitation to Local Search
• Competitive Ratio
• Safely Explorable
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Hill Climbing, once more…• Hill Climbing stores just one state, so it is
an online searcher
• But can not Randomly Restart.– Random Walk– Adding Memory:
• Learning Real Time A*
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Learning Real Time A*• To remember and update the costs of all
visited nodes.
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Learning Real Time A*
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Essay Proposals• What is MA*? Compare it with SMA* and A*.
• What is Tabu Search?
• What is Viterbi Search?
• What is Tree-Trellis?
• What are variants of Genetic Algorithms?
• What is Immunity System Search?
• What is Simulated DNA Computing Search?
• Compare Natural Evolution and Genetic Algorithms.
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Essay Proposals• What is Game of Life?
• What other search method exist that are inspired from nature?
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Essay Proposals• Bring a survey of any of the proposed
algorithms in real applications.
• Bring a detailed usage of one of the algorithms in a real application.
• Choose 5 exercises and do them in details, from any chapter, but tell me first.
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Exercises• Choose one of 4.15 – 4.18
• Due: Esfand 30th
• Email To: [email protected]
• Subject: AIEX-415 .. AIEX-418
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Project Proposals• Make an improvement in one of the
algorithms, compare your results with original one on different domains.
• …
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Project Proposals• Choose a real problem. Design and
implement a suitable algorithm for it. Compare your results with required results or other solutions.
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Project Proposals• Choose a one player game, find some
other people who agree on yours. Write a common engine and perform a competition, with or without time limit.– Mahjongg, Solitaire, Open Tetris, …
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That’s all.