Introduction to Artificial Intelligence (AI) Dr. Mike Bowman Computer Science and Information Systems Department ACM Spring 2014 Acknowledgements Learning Agents Center and Computer Science Department George Mason University lalab.gmu.edu
Association for the Advancement of Artificial Intelligence (AAAI) www.aaai.org 2 AI Thinking Machines? Are you smarter than a machine? Can machines think? What is Skynet? http://www.youtube.com/watch?v=DEtrzdGSXCU What is the Matrix? http://www.youtube.com/watch?v=WnEYHQ9dscY
3 4 How Smart Are You? Can you count the cars in this picture? Can you estimate the time of day in
this picture? 5 How Smart Are You? Can you count the Distribution of letters in a book? Add one thousand 4-digit numbers? Match finger prints? Search a list of a million values for duplicates? 6
AI What is Real? Are you smarter than a machine? Who was Deep Blue? http://www.youtube.com/watch?v=NJarxpYyoFI Who is Watson? http://www.youtube.com/watch?v=o6oS64Bpx0g&feature=related What is the Singularity? http://www.ted.com/talks/ray_kurzweil_announces_singularity_university.html 7
Current Reality? The current reality is that computers and some robots can do some really remarkable complex tasks, and in some cases do them better than humans. But at the same time, no one is yet arguing that machines are conscious or possess human-level intelligence. On the other hand, since no one seems to really understand much about how the human brain works, or what the basis of consciousness is (although some are working on it), the state of AI today is still pretty impressive. 8
Thinking Machines Artificial intelligence (AI) The study of computer systems that attempt to model and apply the intelligence of the human mind For example, writing a program to pick out objects in a picture Where do you often interact with AI? 9 AI in Games The Good, the bad, and the ugly (actually the reality)
Most modern computer/video games include intelligent opposition AI http://www.youtube.com/watch?v=gr58eWO5Dgo http://www.youtube.com/watch?v=tFxbakAamsc http://www.youtube.com/watch?v=Ux0TZqEAiK0 10 The Turing Test Turing test A test to empirically determine whether a computer has achieved intelligence Alan Turing An English mathematician wrote a landmark
paper in 1950 that asked the question: Can machines think? He proposed a test to answer the question "How will we know when weve succeeded?" 11 The Turing Test In a Turing test, the interrogator must determine which responses are from the computer and which are from
the human 12 The Loebner Prize Loebner prize The first formal instantiation (1991) of the Turing test, held annually Chatbots A program designed to carry on a conversation with a human user http://www.loebner.net/Prizef/loebner-prize.html 13
Some Focus Areas of AI Knowledge representation represent knowledge so that a computer can apply it Expert Systems computer systems that embody the knowledge of human experts Neural networks computer systems that mimic the processing of the human brain Natural language processing computers that can understand and use human speech Robotics systems that can complete physical actions to solve problems Just a small sample of AI topics!
14 Knowledge Representation How can we represent knowledge? We need to create a logical view of the data, based on how we want to process it Natural language is very descriptive, but doesnt lend itself to efficient processing Semantic networks and search trees are promising techniques for representing knowledge 15
Semantic Networks 16 Knowledge Representation and Reasoning Intelligent agents contain a representation of an application domain, where information about the domain (objects, relations, classes, laws, actions) are represented as symbolic expressions. This mapping allows the agent to reason about the domain by performing reasoning processes in the domain model, and transferring the conclusions back into the application domain. ONTOLOGY OBJECT
SUBCLASS-OF represents BOOK CUP TABLE INSTANCE-OF If an object is on top of another object that is
itself on top of a third object then the first object is also on top of the third object. CUP1 ON BOOK1 ON TABLE1
RULE x,y,z OBJECT, (ON x y) & (ON y z) (ON x z) Application Domain 2005, G.Tecuci, Learning Agents Center Model of the Domain 17 Knowledge Base = Ontology + Rules PROBLEM
SOLVING TASK Determine whether John Smith can be a PhD advisor for Tom Even in Artificial Intelligence. person subconcept-of university employee subconcept-of ONTOLOGY FRAGMENT
student subconcept-of IF: Determine whether ?O1 can be a PhD advisor for ?O2 in ?O3. faculty undergraduate staff Main condition member PhD_advisor student member ?O1 is
PhD_advisor subconcept-of has_as_employer ?O4 subconcept-of has_as_position ?O5 professor graduate ?O2 is PhD_student instructor student B.S. student
?O3 is research_area subconcept-of ?O4 is university subconcept-of ?O5 is tenured_position assistant full Except when condition professor professor Ph.D. student
?O1 is person associate is_likely_to_move_to ?O6 M.S. student ?O6 is employer professor THEN: Determine whether ?O1 would be a good PhD advisor for ?O2 in ?O3. REASONING RULE
2005, G.Tecuci, Learning Agents Center 18 Search Trees Search tree A structure that represents alternatives in adversarial situations such as game playing The paths down a search tree represent a series of decisions made by the players 19 Search Trees
20 Search Trees Techniques for pruning search space Depth-first A technique that involves the analysis of selected paths all the way down the tree Breadth-first A technique that involves the analysis of all possible paths but only for a short distance down the tree Breadth-first tends to yield the best results
21 Search Trees Depth-first and breadth-first searches 22 Expert Systems Knowledge-based system Software that uses a specific set of information, from which it extracts and processes particular pieces Expert system
A software system based on the knowledge of human experts; it is Rule-based system A software system based on a set of if-then rules Inference engine The software that processes rules to draw conclusions 23 Expert Systems Gardner Expert System Example 24
Expert Systems Named abbreviations that represent conclusions NONE apply no treatment at this time TURF apply a turf-building treatment WEED apply a weed-killing treatment BUG apply a bug-killing treatment FEED apply a basic fertilizer treatment WEEDFEED apply a weed-killing and fertilizer combination treatment 25 Expert Systems
26 Expert Systems Some rules if (CURRENT LAST < 30) then NONE if (SEASON = winter) then not BUGS if (BARE) then TURF if (SPARSE and not WEEDS) then FEED if (BUGS and not SPARSE) then BUG if (WEEDS and not SPARSE) then WEED if (WEEDS and SPARSE) then WEEDFEED 27
Expert Systems Operation of expert systems can involve dialog between a human and the system System: areas? User: System: bugs? User: System: User: System: weeds? User:
System: Does the lawn have large, bare No Does the lawn show evidence of No Is the lawn generally thin? Yes Does the lawn contain significant Yes You should apply a weed-killing and fertilizer combination treatment. 28
Neural Networks Artificial neural networks An attempt to represent knowledge, and solve problems, with approaches that mimic the way the human brain works. often involves probabilities and statistics to determine (guess?) most likely outcomes. 29 Natural versus Artificial Biological
Artificial http://research.yale.edu/ysm/images/78.2/articles-neural-neuron.jpg http://faculty.washington.edu/chudler/color/pic1an.gif 30 Natural Language Processing Three basic types of processing occur during human/computer voice interaction Voice synthesis Using a computer to create the sound of human speech Voice recognition Using a computer to recognizing the words spoken by a
human Natural language comprehension Using a computer to apply a meaningful interpretation to human communication 31 Natural Language Comprehension What does this sentence mean? Time flies like an arrow. Time goes by quickly Time flies (using a stop watch to measure speed of a fly) as you would time an arrow Time flies (a kind of fly) are fond of an arrow
Crazy? Maybe, but a computer has great difficulty with understanding context. 32 Rules and Machine Learning Imagine a simple rule such as: A+ B+ C = D It could represent the rule an agent uses to identify a cat: (4 legs) + (fur) + (meow) = CAT A sophisticated agent learns. If presented with a dog [(4 legs) + (fur) + (bark) = ??] It should recognize it as not being a cat, and ask a human trainer for clarification, resulting in a new rule:
A + B + E = F (4 legs) + (fur) + (bark) = DOG 33 Rules and Machine Learning Start with the same rule: A+ B+ C = D It could represent the rule an agent uses to identify a cat: (4 legs) + (fur) + (meow) = CAT A very sophisticated agent learns rapidly. It might generalize the rule to (~A) + (~B) + (~C) = CAT It will occasionally miss-fire (ID a dog as a cat) by misusing the generalized rule, but given supervision and corrections by humans (explaining why it misused the rule), it will learn rapidly.
34 State of the Art in AI IBMs Deep Blue Deep Blue was a special purpose computer that was able to calculate nearly a quarter of a billion chess positions per second. It lost a match with a world champion in 1996, but defeated that same champion in 1997. Certain endgame arrangements were always thought to represent a draw -no human had ever seen a way to win. Deep Blue and other chess playing programs have found ways to win some of these games. The Asian game "go" will not succumb to such brute force methods -there are simply too many possible moves for even the most powerful current and planned supercomputers. Instead, "real" AI will be needed -intelligence based on pattern recognition, "insight," and strategy.
35 State of the Art in AI IMBs Watson Jeopardy Champion Research question: Can a system be designed that applies advanced data management and analytics to natural language in order to uncover a single, reliable insight in a fraction of a second? Watson beat human champions in Jeopardy, and is now being used to investigate human health issues. 36
State of the Art in AI DARPA Grand Challenge The DARPA Grand Challenge tasked entrants to build an autonomous robotic vehicle that could successfully navigate a long, complex course, without human intervention. None of the entrants in the 2004 Grand Challenge 1 completed more than 10 miles of the 100 mile course. In the 2005 Grand Challenge II, 5 of the 20+ entrants completed the 132 mile race through the Mojave Desert, well under the 10 hour time limit. The 2007-2010 Urban Challenge was to drive through an urban setting. It was successfully
completed by multiple entrants! 37 State of the Art in AI DARPA Robotics Challenge The latest DARPA event is called the Robotics Challenge and tasks entrants to build an all-terrain autonomous device that can successfully navigate through complex obstacles like stairs, without human intervention AND successfully complete missions that are hazardous to humans. 38
AI versus Bowman Where has a famous AI system and someone named Bowman crossed paths? http://www.youtube.com/watch?v=ARJ8cAGm6JE CSC370 Fall 2014 Tuesday and Thursday 2:00-3:15 PM 39
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