Revised By: Ghulam Irtaza SheikhAman Ullah KhanA.I. IS THE FUTURE OF COMPUTING!

Text:Artificial Intelligence: Structures and Strategies for Complex Problem Solving by GEORGE F LUGERReference:Practical Common Lisp by Peter Seibel Learn Prolog Now, by Patrick Blackburn, Johan Bos and Kristina StriegnitzCLIPS User and Reference ManualsVarious resources on the Web CS 607 (VU)

Course TopicsWeek 1: Chapter 1 AI: History and applicationsWeek 2: Chapter 2 -- The predicate calculusWeek 3: Chapter 3 -- Structures and strategies for state space searchWeek 4 & 5: Chapter 14 -- An introduction to PrologWeek 6: Chapter 4 Heuristic searchWeek 7: Chapter 5 Architectures for AI problem solvingWeek 8: MakeupWeek 9: Midterm Examination

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What is AI?

Brief History of AI

What is this course?

An Attempted DefinitionAI the branch of computer science that is concerned with the automation of intelligent behaviortheoretical and applied principlesData structures for knowledge representationAlgorithms of applying knowledgeLanguages for algorithm implementation

ProblemWhat is Intelligence?

This course discussesThe collection of problems and methodologies studied by AI researchers

Brief Early History of AIAristotle 2000 years agoThe nature of worldLogicsModus ponens and reasoning systemCopernicus 1543Split between human mind and its surroundingsDescrates (1680)Thought and mindSeparate mind from physical worldMental process formalized by mathematics

Modern HistoryFormal logicLeibnizBooleTuringFrege first-order predicate calculusGraph theoryEulerState space search

Models of IntelligenceLogic ModelsFormal logicFuzzy logicNon-monotonic logic

What is AI?The science of making machines that:

Think like humansThink rationallyAct like humansAct rationally

Scientific Goals of AI Institute of ComputingAI seeks to understand the working of the mind in mechanistic terms, just as medicine seeks to understand the working of the body in mechanistic terms. The mind is what the brain does. -- Marvin Minsky The strong AI position is that any aspect of human intelligence could, in principle, be mechanized

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The Turing TestCSC411Artificial Intelligence*If the interrogator cannot distinguish the machine from the human, then the machine may be assumed to be intelligent.The interrogator cannot see and speak to either does not know which is actually machineMay communicate with them solely by textual device

Artificial Intelligence

Acting Like Humans?Turing (1950) ``Computing machinery and intelligence''``Can machines think?'' ``Can machines behave intelligently?''Operational test for intelligent behavior: the Imitation Game

Predicted by 2000, a 30% chance of fooling a lay person for 5 minutesAnticipated all major arguments against AI in following 50 yearsSuggested major components of AI: knowledge, reasoning, language understanding, learning

Imaging the Brain

Brains ~ Computers1000 operations/sec100,000,000,000 unitsstochasticfault tolerantevolves, learns1,000,000,000 ops/sec1-100 processorsdeterministiccrashesdesigned, programmed

Areas of Artificial Intelligence Institute of ComputingPerception Machine vision Speech understanding Touch ( tactile or haptic) sensation Natural Language Processing Natural Language Understanding Speech Understanding Language Generation Machine Translation

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Areas of Artificial Intelligence ...Institute of ComputingRobotics Planning Expert Systems Machine Learning Theorem Proving Symbolic Mathematics Game Playing

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Perception Institute of ComputingMachine Vision: It is easy to interface a TV camera to a computer and get an image into memory; the problem is understanding what the image represents. Vision takes lots of computation; in humans, roughly 10% of all calories consumed are burned in vision computation. Speech Understanding: Speech understanding is available now. Some systems must be trained for the individual user and require pauses between words. Understanding continuous speech with a larger vocabulary is harder. Touch ( tactile or haptic) Sensation:Important for robot assembly tasks.

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Robotics Institute of ComputingAlthough industrial robots have been expensive, robot hardware can be cheap: Radio Shack has sold a working robot arm and hand for $15. The limiting factor in application of robotics is not the cost of the robot hardware itself. What is needed is perception and intelligence to tell the robot what to do; ``blind'' robots are limited to very well-structured tasks (like spray painting car bodies).

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Natural Language UnderstandingInstitute of Computing

Natural languages are human languages such as English. Making computers understand English allows non-programmers to use them with little training. Applications in limited areas (such as access to data bases) are easy. (askr '(where can i get ice cream in berkeley))Natural Language Generation: Easier than NL understanding. Can be an inexpensive output device. Machine Translation: Usable translation of text is available now. Important for organizations that operate in many countries. In a not too far future develops for eleven-year old David in a research lab the first intelligent robot with human feelings in the shape. But its "foster parents" are overtaxed with the artificial spare child and suspend it. Posed on itself alone David tries to fathom its origin and the secret of its existence.

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PlanningInstitute of ComputingPlanning attempts to order actions to achieve goals. Planning applications include logistics, manufacturing scheduling, planning manufacturing steps to construct a desired product. There are huge amounts of money to be saved through better planning.

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Expert Systems Institute of ComputingExpert Systems attempt to capture the knowledge of a human expert and make it available through a computer program. There have been many successful and economically valuable applications of expert systems. Benefits: Reducing skill level needed to operate complex devices. Diagnostic advice for device repair. Interpretation of complex data. Cloning'' of scarce expertise. Capturing knowledge of expert who is about to retire. Combining knowledge of multiple experts. Intelligent training.

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Theorem ProvingInstitute of Computing Proving mathematical theorems might seem to be mainly of academic interest. However, many practical problems can be cast in terms of theorems. A general theorem prover can therefore be widely applicable. Examples: Automatic construction of compiler code generators from a description of a CPU's instruction set. J Moore and colleagues proved correctness of the floating-point division algorithm on AMD CPU chip.

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Symbolic MathematicsInstitute of ComputingSymbolic mathematics refers to manipulation of formulas, rather than arithmetic on numeric values. Algebra Differential and Integral Calculus Symbolic manipulation is often used in conjunction with ordinary scientific computation as a generator of programs used to actually do the calculations. Symbolic manipulation programs are an important component of scientific and engineering workstations. > (solvefor '(= v (* v0 (- 1 (exp (- (/ t (* r c))))))) 't)(= T (* (- (LOG (- 1 (/ V V0)))) (* R C)))

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Game Playing Institute of ComputingGames are good vehicles for research because they are well formalized, small, and self-contained. They are therefore easily programmed. Games can be good models of competitive situations, so principles discovered in game-playing programs may be applicable to practical problems.

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Characteristics of A.I. ProgramsInstitute of Computing Symbolic Reasoning: reasoning about objects represented by symbols, and their properties and relationships, not just numerical calculations. Knowledge: General principles are stored in the program and used for reasoning about novel situations. Search: a ``weak method'' for finding a solution to a problem when no direct method exists. Problem: combinatoric explosion of possibilities. Flexible Control: Direction of processing can be changed by changing facts in the environment.

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Symbolic ProcessingInstitute of ComputingMost of the reasoning that people do is non-numeric. AI programs often do some numerical calculation, but focus on reasoning with symbols that represent objects and relationships in the real world. Objects. Properties of objects. Relationships among objects. Rules about classes of objects. Examples of symbolic processing: Understanding English: (show me a good chinese restaurant in los altos)Reasoning based on general principles: if: the patient is malethen: the patient is not pregnantSymbolic mathematics: If y = m*x+b, what is the derivativeof y with respect to x?

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Knowledge Representation Institute of ComputingIt is necessary to represent the computer's knowledge of the world by some kind of data structures in the machine's memory. Traditional computer programs deal with large amounts of data that are structured in simple and uniform ways. A.I. programs need to deal with complex relationships, reflecting the complexity of the real world. Several kinds of knowledge need to be represented: Factual Data: Known facts about the world. General Principles: ``Every dog is a mammal.'' Hypothetical Data: The computer must consider hypotheticals in order to reason about the effects of actions that are being contemplated.

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What can AI do?

Representation

Search

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What can AI do?

Representation

Search

Representation SystemsWhat is it?Capture the essential features of a problem domain and make that information accessible to a problem-solving procedureMeasuresAbstraction how to manage complexityExpressiveness what can be representedEfficiency how is it used to solve problemsTrade-off between efficiency and expressiveness

Different representations of the real number . Representation of

Logical Clauses describing some important properties and relationshipsGeneral ruleA blocks worldBlock World Representation

Logical predicates representing a simple description of a bluebird.Bluebird RepresentationsSemantic network description of a bluebird.

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What can AI do?

Representation

Search

State Space SearchState spaceState any current representation of a problemState spaceAll possible state of the problemStart states the initial state of the problemTarget states the final states of the problem that has been solvedState space graph Nodes possible statesLinks actions that change the problem from one state to anotherState space searchFind a path from an initial state to a target state in the state spaceVarious search strategiesExhaustive search guarantee that the path will be found if it exists Depth-firstBreath-firstBest-first searchheuristics

Portion of the state space for tic-tac-toe.Tic-tac-toe State Space

State space description of the automotive diagnosis problem. Auto Diagnosis State Space

AssignmentCreate and justify your own definition of artificial intelligence? Discuss whether or not you think it is possible to a computer to understand and use a natural?Discuss why you think the problem of machines "learning" is so difficult.?

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