Decision Tree for Quake

فهرست عناوین اصلی در این پاورپوینت

فهرست عناوین اصلی در این پاورپوینت

● Today
● Classification
● Decision Trees
● Handling Simultaneous Actions
● Deciding on Actions
● Sense, Think, Act Cycle
● Decision Tree for Quake
● Compare and Contrast
● Building Decision Trees
● Learning Decision Trees
● Induction
● Learning Algorithms
● Induction requires Examples
● Decision Tree Advantages
● Decision Tree Disadvantages
● References
● Rule-Based Systems
● Rule-Based Systems Structure
● AI Cycle
● Age of Kings
● Implementing Rule-Based Systems
● Efficient Special Case
● General Case
● Baulders Gate
● Research Rule-based Systems
● Conflict Resolution Strategies
● Basic Idea of Efficient Matching
● Rule-based System: Good and Bad
● References

نوع زبان: انگلیسی حجم: 0.15 مگا بایت
نوع فایل: اسلاید پاورپوینت تعداد اسلایدها: 31 صفحه
سطح مطلب: نامشخص پسوند فایل: ppt
گروه موضوعی: زمان استخراج مطلب: 2019/05/10 01:19:29

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عبارات مهم استفاده شده در این مطلب

عبارات مهم استفاده شده در این مطلب

tree, e, decision, action, example, l, s, state, c, d, fall, ۶۳۸,

توجه: این مطلب در تاریخ 2019/05/10 01:19:29 به صورت خودکار از فضای وب آشکار توسط موتور جستجوی پاورپوینت جمع آوری شده است و در صورت اعلام عدم رضایت تهیه کننده ی آن، طبق قوانین سایت از روی وب گاه حذف خواهد شد. این مطلب از وب سایت زیر استخراج شده است و مسئولیت انتشار آن با منبع اصلی است.

http://research.cs.wisc.edu/graphics/Courses/638-f2001/lectures/cs638-17.ppt

در صورتی که محتوای فایل ارائه شده با عنوان مطلب سازگار نبود یا مطلب مذکور خلاف قوانین کشور بود لطفا در بخش دیدگاه (در پایین صفحه) به ما اطلاع دهید تا بعد از بررسی در کوتاه ترین زمان نسبت به حدف با اصلاح آن اقدام نماییم. جهت جستجوی پاورپوینت های بیشتر بر روی اینجا کلیک کنید.

عبارات پرتکرار و مهم در این اسلاید عبارتند از: tree, e, decision, action, example, l, s, state, c, d, fall, ۶۳۸,

مشاهده محتوای متنیِ این اسلاید ppt

مشاهده محتوای متنیِ این اسلاید ppt

today ai decision trees rule based systems cs ۶۳۸ fall ۲ ۱ classification our aim is to decide which action to take given the world state convert this to a classification problem the state of the world is a set of attributes or features who i can see how far away they are how much energy … given any state there is one appropriate action extends to multiple actions at the same time the action is the class that a world state belongs to low energy see the enemy means i should be in the retreat state classification problems are very well studied cs ۶۳۸ fall ۲ ۱ decision trees nodes represent attribute tests one child for each possible outcome of the test leaves represent classifications can have the same classification for several leaves classify by descending from root to a leaf at each node perform the test and descend the appropriate branch when a leaf is reached return the classification action of that leaf decision tree is a disjunction of conjunctions of constraints on the attribute values of an instance action if a and b and c or a and ~b and d or … … retreat if low health and see enemy or low health and hear enemy or … … cs ۶۳۸ fall ۲ ۱ handling simultaneous actions treat each output command as a separate classification problem given inputs should walk forward backward stop given inputs should turn left right none given inputs should run yes no given inputs should weapon blaster shotgun… given inputs should fire yes no have a separate tree for each command if commands are not independent need a conflict resolution strategy can’t run and stop for instance cs ۶۳۸ fall ۲ ۱ deciding on actions each time the ai is called poll each decision tree for current output event driven only call when state changes need current value of each input attribute all sensor inputs describe the state of the world store the state of the environment most recent values for all sensor inputs change state upon receipt of a message or check validity when ai is updated or a mix of both polling and event driven cs ۶۳۸ fall ۲ ۱ sense think act cycle sense gather input sensor changes update state with new values think poll each decision tree act execute any changes to actions sense think act cs ۶۳۸ fall ۲ ۱ decision tree for quake just one tree attributes e t f l t f s t f d t f actions attack retreat chase spawn wander could add additional trees if i’m attacking which weapon should i use if i’m wandering which way should i go much like hierarchical fsms d spawn e l s wander retreat attack l t t t f f f retreat chase t f f t cs ۶۳۸ fall ۲ ۱ compare and contrast spawn d e s l wander e d s l e s attack e e d s l e chase e d s l s d s d d retreat e e d s l l e retreat s e d s l wander l e d s l retreat es e d s l attack es e d s l e e e l s s l e e l l l l l d cs ۶۳۸ fall ۲ ۱ assume is no arc just stay in same state. how about if add select weapon – actions that are supposed to happen in every state such as pick best weapon – how get back to original state. if have details of wander every state must be connected to all others. building decision trees decision trees can be constructed by hand think of the questions you would ask to decide what to do for example tonight i can study play games or sleep. how do i make my decision but decision trees are typically learned provide examples many sets of attribute values and resulting actions algorithm then constructs a tree from the examples reasoning we don’t know how to decide on an action so let the computer do the work whose behavior would we wish to learn cs ۶۳۸ fall ۲ ۱ learning decision trees decision trees are usually learned by induction generalize from examples induction doesn’t guarantee correct decision trees bias towards smaller decision trees occam’s razor prefer simplest theory that fits the data too expensive to find the very smallest decision tree learning is non incremental need to store all the examples id۳ is the basic learning algorithm c۴.۵ is an updated and extended version cs ۶۳۸ fall ۲ ۱ induction if x is true in every example that results in action a then x must always be true for action a more examples are better errors in examples cause difficulty if x is true in most examples x must always be true id۳ does a good job of handling errors noise in examples note that induction can result in errors it may just be coincidence that x is true in all the examples typical decision tree learning determines what tests are always true for each action assumes that if those things are true again then the same action should result cs ۶۳۸ fall ۲ ۱ learning algorithms recursive algorithms find an attribute test that separates the actions divide the examples based on the test recurse on the subsets what does it mean to separate ideally there are no actions that have examples in both sets failing that most actions have most examples in one set the things to measure is entropy the degree of homogeneity or lack of it in a set entropy is also important for compression what have we seen before that tries to separate sets why is this different cs ۶۳۸ fall ۲ ۱ induction requires examples where do examples come from programmer designer provides examples capture an expert player’s actions and the game state while they play of examples need depends on difficulty of concept difficulty number of tests needed to determine the action more is always better training set vs. testing set train on most ۷۵ of the examples use the rest to validate the learned decision trees by estimating how well the tree does on examples it hasn’t seen cs ۶۳۸ fall ۲ ۱ decision tree advantages simpler more compact representation state is recorded in a memory create internal sensors – enemy recently sensed easy to create and understand can also be represented as rules decision trees can be learned cs ۶۳۸ fall ۲ ۱ decision tree disadvantages decision tree engine requires more coding than fsm each tree is unique sequence of tests so little common structure need as many examples as possible higher cpu cost but not much higher learned decision trees may contain errors cs ۶۳۸ fall ۲ ۱ references mitchell machine learning mcgraw hill ۱۹۹۷ russell and norvig artificial intelligence …

کلمات کلیدی پرکاربرد در این اسلاید پاورپوینت: tree, e, decision, action, example, l, s, state, c, d, fall, ۶۳۸,

این فایل پاورپوینت شامل 31 اسلاید و به زبان انگلیسی و حجم آن 0.15 مگا بایت است. نوع قالب فایل ppt بوده که با این لینک قابل دانلود است. این مطلب برگرفته از سایت زیر است و مسئولیت انتشار آن با منبع اصلی می باشد که در تاریخ 2019/05/10 01:19:29 استخراج شده است.

http://research.cs.wisc.edu/graphics/Courses/638-f2001/lectures/cs638-17.ppt

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