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Machine Learning

This overview of machine learning touches on key concepts like supervised and unsupervised learning, online versus offline learning, and various classification methods. It emphasizes the importance of version spaces and decision trees, illustrating how negative instances help prevent overgeneralization. The text also discusses expert systems, knowledge bases, and the challenges associated with knowledge acquisition. With real-world applications in gaming, such as strategic decision-making in Age of Empires, we highlight how machine learning can optimize performance and save costs.

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Machine Learning

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  1. Machine Learning A Quick look • Sources: • Artificial Intelligence – Russell & Norvig • Artifical Intelligence - Luger By: Héctor Muñoz-Avila

  2. Action1 Action2 Knowledge Knowledge changed What Is Machine Learning? “Logic is not the end of wisdom, it is just the beginning” --- Spock time Game Game System System

  3. Learning: The Big Picture • Two forms of learning: • Supervised: the input and output of the learning component can be perceived (for example: experienced player giving friendly teacher) • Unsupervised: there is no hint about the correct answers of the learning component (for example to find clusters of data)

  4. Offline vs. Online Learning • Online – during gameplay • Adapt to player tactics • Avoid repetition of mistakes • Requirements: computationally cheap, effective, robust, fast learning (Spronck 2004) • Offline– Between the end of a game and the next • Devise new tactics • Discover exploits

  5. Classification(According to the language representation) • Symbolic • Version Spaces • Decision Trees • Explanation-Based Learning • … • Sub-symbolic • Reinforcement Learning • Connectionist • Evolutionary

  6. Classification(According to the language representation) • Symbolic • Version Space • Decision Trees • Explanation-Based Learning • … • Sub-symbolic • Reinforcement Learning • Connectionist • Evolutionary

  7. Two extremes (temptative) solutions: too general obj(X,Y,Z) … obj(X,Y,ball) concept space obj(large,Y,ball) obj(small,Y,ball) • obj(large,white,ball) obj(small,blue,ball) … too specific Version Space Idea: Learn a concept from a group of instances, some positive and some negative • Example: • target: obj(Size,Color,Shape) • Size = {large, small} • Color = {red, white, blue} • Shape = {ball, brick, cube} • Instances: • +: • obj(large,white,ball) • obj(small,blue,ball) • −: • obj(small,red,brick) • obj(large,blue,cube)

  8. How Version Space Works If we consider positive and negatives If we consider only positives + + + + + + + + + + + + + − + − − − + + What is the role of the negative instances? to help prevent over-generalizations

  9. Classification(According to the language representation) • Symbolic • Version Space • Decision Trees • Explanation-Based Learning • … • Sub-symbolic • Reinforcement Learning • Connectionist • Evolutionary

  10. Explanation-Based learning C A B C A B C B A B A C ? B A ? C B ? A B C C B A B A C A C A A B C B C C B A A B C Can we avoid making this error again?

  11. Possible rule: If the initial state is this and the final state is this, don’t do that Explanation-Based learning (2) C A B ? B A ? C ? A B C C B A B A C More sensible rule: don’t stack anything above a block, if the block has to be free in the final state

  12. Classification(According to the language representation) • Symbolic • Version Space • Decision Trees • Explanation-Based Learning • … • Sub-symbolic • Reinforcement Learning • Connectionist • Evolutionary

  13. Motivation # 1: Analysis Tool • Suppose that a gaming company have a data base of runs with a beta version of the game, lots of data • How can that company’s developers use this data to figure out an good strategies for their AI

  14. Decision Tree induction “if built center hall & has built 4 workers then build defense tower” Motivation # 1: Analysis Tool (cont’d) Games data

  15. The Knowledge Base in Expert Systems A knowledge base consists of a collection of IF-THEN rules: if built center hall & has built 4 workers then build defense tower if built center hall & mine then upgrade center hall Knowledge bases of expert systems contain hundreds and sometimes even thousands such rules. Frequently rules are contradictory and/or overlap

  16. Sample Expert System in Games: Age of Empires http://www.youtube.com/watch?v=GEbnqc82lew (defrule �(current-age == dark-age �(building-type-count-total mining-camp > 0) �(not (research-available feudal-age)) �=> �(set-strategic-number sn-food-gatherer-percentage 52) �(set-strategic-number sn-wood-gatherer-percentage 35) �(set-strategic-number sn-gold-gatherer-percentage 13) �(set-strategic-number sn-stone-gatherer-percentage 0) �(disable-self) )

  17. Main Drawback of Expert Systems: The Knowledge Acquisition Bottle-Neck The main problem of expert systems is acquiring knowledge from human specialist is a difficult, cumbersome and long activity. KB = Knowledge Base KA = Knowledge Acquisition

  18. Motivation # 2: Avoid Knowledge Acquisition Bottle-Neck • GASOIL is an expert system for designing gas/oil separation systems stationed of-shore • The design depends on multiple factors including: • proportions of gas, oil and water, flow rate, pressure, density, viscosity, temperature and others • To build that system by hand would had taken 10 person years • It took only 3 person-months by using inductive learning! • GASOIL saved BP millions of dollars

  19. Motivation # 2 : Avoid Knowledge Acquisition Bottle-Neck KB = Knowledge Base KA = Knowledge Acquisition IDT = Induced Decision Trees

  20. Full none some no yes waitEstimate? 0-10 >60 30-60 10-30 no Alternate? Hungry? yes Yes no yes No yes Alternate? Reservation? Fri/Sat? yes yes no no yes no yes Raining? No Yes Bar? Yes yes no no yes yes No no Yes Example of a Decision Tree Patrons?

  21. Definition of A Decision Tree A decision tree is a tree where: • The leaves are labeled with classifications (if the classification is “yes” or “no”. The tree is called a boolean tree) • The non-leaves nodes are labeled with attributes • The arcs out of a node labeled with an attribute A are labeled with the possible values of the attribute A

  22. Databases: what are the data that matches this pattern? Induction: what is the pattern that matches these data? database induction Induction Data pattern

  23. Induction of Decision Trees • Objective: find a concise decision tree that agrees with the examples • The guiding principle we are going to use is the Ockham’s razor principle: the most likely hypothesis is the simplest one that is consistent with the examples • Problem: finding the smallest decision tree is NP-complete • However, with simple heuristics we can find a small decision tree (approximations)

  24. Induction of Decision Trees: Algorithm • Algorithm: • Initially all examples are in the same group • Select the attribute that makes the most difference (i.e., for each of the values of the attribute most of the examples are either positive or negative) • Group the examples according to each value for the selected attribute • Repeat 1 within each group (recursive call)

  25. Example

  26. Patrons? full none some X4(+),x12(+), x2(-),x5(-),x9(-),x10(-) X7(-),x11(-) X1(+),x3(+),x6(+),x8(+) Type? burger italian french thai X3(+),x12(+), x7(-),x9(-) X6(+), x10(-) X1(+), x5(-) X4(+),x12(+) x2(-),x11(-) IDT: Example Lets compare two candidate attributes: Patrons and Type. Which is a better attribute?

  27. Full none some no yes waitEstimate? 0-10 >60 30-60 10-30 no Alternate? Hungry? yes Yes no yes No yes Alternate? Reservation? Fri/Sat? yes yes no no yes no yes Raining? No Yes Bar? Yes yes no no yes yes No no Yes Example of a Decision Tree Patrons?

  28. Decision Trees in Gaming http://www.youtube.com/watch?v=HMdOyUp5Rvk • Black & White, developed by Lionhead Studios, and released in 2001 • Used to predict a player’s reaction to a certain creature’s action • In this model, a greater feedback value means the creature should attack • This is done by inducing a decision tree

  29. Decision Trees in Black & White should your creature attack a town?

  30. Decision Trees in Black & White Allegiance Friendly Enemy Defense -1.0 Weak Strong Medium 0.4 -0.3 0.1 Note that this decision tree does not even use the tribe attribute

  31. Decision Trees in Black & White • Now suppose we don’t want the entire decision tree, but we just want the 2 highest feedback values • We can create a Boolean expressions, such as ((Allegiance = Enemy) ^ (Defense = Weak)) v ((Allegiance = Enemy) ^ (Defense = Medium))

  32. Classification(According to the language representation) • Symbolic • Version Space • Decision Trees • Explanation-Based Learning • … • Sub-symbolic • Reinforcement Learning • Connectionist • Evolutionary Next class

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