more precisely called branch of ai behind it
TRANSCRIPT
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A HUMAN –LEVELA HUMAN –LEVELARTIFICIAL ARTIFICIAL
INTELLIGENCEINTELLIGENCEAPPLICATIONAPPLICATION
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More precisely called
Branch of AI behind it
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are Interactive games an area of Human-level AI research ?
is AI used in Interactive games ?
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Picture Courtesy : Google Images
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Human -like attributes expected Human -like attributes expected in a human-level ai system…in a human-level ai system…
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are Interactive games an area of Human-level AI research ?
is AI used in Interactive games ?
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Search
Planning
Logic
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Focus : Game Tactics
A case study : the basicsA case study : the basics
How AI is used to enhance Game Tactics
How AI is used to enhance Game Tactics
AI tools used
Evolutionary computation &
Reinforcement Learning
Real-time Strategy Games
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Genetic Algorithm
A learning technique with a mathematical reward function.
A learning technique with a mathematical reward function.
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• Player needs to control armies to defeat all opposing forces in a virtual battlefield.
• Key to winning lies in efficiently collecting and managing resources., and appropriately allocating these resources over various action elements.
• Famous examples : Age Of Empires , World of Warcraft .Picture Courtesy : http://www.igniq.com/images/age_of_empires_3
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Improve
Weaponry Attack
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• AI in RTS games determines all decisions of the computer opponents.
• Encoded in the form of scripts. Called STATIC SCRIPTS
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I don’t care about available
resources. Attack at earliest !!!Ha Ha Ha!!
I have to first well develop my army,
then only I can attack. This will
take a while.
HUMAN
AI
Picture Courtesy : World Of Warcraft
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I have suffered heavy losses. Now I need to increase my
strength first. Small attacks are
of no use.
AI is gathering resources and preparing for
heavy assault.
HUMAN
AI
Picture Courtesy : World Of Warcraft
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)()(
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,
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C end is a parameter and is set less than 0.5.
Contribution of State Reward is kept larger than Global Reward.
P max and R max are the maximum penalty and maximum reward respectively.
}{1)1(
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max
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bRb
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bRb
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Wi
endend
iendend
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Evolutionary State Based Tactics Generator (ESTG)
Genetic Algorithm Application !!!
Counter Strategies are “played” against training scripts , only the fittest are allowed to the next generation.
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Chromosome EncodingEA works with a population of chromosomes . Each represents a static strategy .
The chromosome is divided into the m states .
Start State 1 State 2 State m End
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States include a state marker followed by the state number and a series of genes.
Chromosome Encoding
A Gene
Parameter values
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4 types of genes
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Partial example of a chromosome .
Chromosome Encoding
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Fitness Function
b
MM
M
bMM
M
C
C
F
sa
a
sa
aT
,max
,minmax
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Fitness Function
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Genetic Operators
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Genetic Operators
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KT: State-based Knowledge Transfer
The possible tactics during a game mainly depend on the available units and technology, which in RTS games typically depend on the buildings that the player possesses.
Thus, we distinguish tactics using the Wargus states .
All genes grouped in an activated state (which includes at least one activated gene) in the chromosomes are considered to be a single tactic.
tactics
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Extracting Tactics for a state
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Performance of Dynamic Scripting Experiment Scenario
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Performance Analysis
The three bars that reached 100 represent runs where no RTP was found (e.g., dynamic scripting was unable to statistically outperform the specified opponent).
The opponent strategies
Ave
rag
e R
TP
valu
eRTP is the number of the first game in which the adaptive agent outperforms the static agent.
low RTP value indicates good efficiency for dynamic scripting
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Where we stand Where we stand today………today………
Achieved
Achieved
Achieved
Achieved
Achieved
Achieved
Not Achieved
Not Achieved
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Picture Courtesy : Prince Of Persia , Google Images
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DrawbacksDrawbacks
Giving undue advantages to AI agents.
Future – Scope:Future – Scope:
• Removing the “cheating” factor from Interactive games.
• Introduction of Creativity in AI agents.
• Capability of AI agents to reason with human-like Common Sense.
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Ponsen,M. & Spronck,P.(2006). Automatically Generating Game Tactics via Evolutionary Learning.
Spronck,P. , Sprinkhuizen Kuyper,I. & Postma,E. (2004).Online adaptation of game opponent AI with dynamic scripting.
Sutton,R., & Barto,A.(1998). Reinforcement learning : an introduction.
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