Presentation on theme: "Artificial Intelligence (AI)"— Presentation transcript:
1 Artificial Intelligence (AI) A.I. is the Future of Computing!Artificial Intelligence (AI)Aman Ullah KhanRevised By: Ghulam Irtaza Sheikh
2 Text:Artificial Intelligence: Structures and Strategies for Complex Problem Solving by GEORGE F LUGERReference:Practical Common Lisp by Peter SeibelLearn Prolog Now, by Patrick Blackburn, Johan Bos and Kristina StriegnitzCLIPS User and Reference ManualsVarious resources on the WebCS 607 (VU)
3 Course Topics Week 1: Chapter 1 – AI: History and applications Week 2: Chapter 2 -- The predicate calculusWeek 3: Chapter 2– First order predicate calculus &Unification.Week 4 & 5: Chapter 3 – Structure and strategies for state space searchWeek 6: Chapter 4 – Heuristic searchWeek 7: Chapter 5 – Architectures for AI problem solvingWeek 8: MakeupWeek 9: Midterm Examination
4 TodayWhat is AI?Brief History of AIWhat is this course?
5 An Attempted Definition AI – 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 implementationProblemWhat is Intelligence?This course discussesThe collection of problems and methodologies studied by AI researchers
6 Brief Early History of AI Aristotle – 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
7 Modern History Formal logic Graph theory State space search Leibniz BooleTuringFrege – first-order predicate calculusGraph theoryEulerState space search
8 What is AI? The science of making machines that: Think like humans Think rationallyAct like humansAct rationally
9 The mind is what the brain does. -- Marvin Minsky Scientific Goals of AIAI 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 MinskyThe strong AI position is that any aspect of human intelligence could, in principle, be mechanizedInstitute of Computing
10 The Turing Test The interrogator cannot see and speak to either does not know which is actually machineMay communicate with them solely by textual deviceIf the interrogator cannot distinguish the machine from the human, then the machine may be assumed to be intelligent.Artificial IntelligenceCSC411
11 Acting Like Humans?Turing (1950) ``Computing machinery and intelligence''``Can machines think?'' ``Can machines behave intelligently?''Operational test for intelligent behavior: the Imitation GamePredicted 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
14 Areas of Artificial Intelligence PerceptionMachine visionSpeech understandingTouch ( tactile or haptic) sensationNatural Language ProcessingNatural Language UnderstandingSpeech UnderstandingLanguage GenerationMachine TranslationInstitute of Computing
15 Areas of Artificial Intelligence ... RoboticsPlanningExpert SystemsMachine LearningTheorem ProvingSymbolic MathematicsGame PlayingInstitute of Computing
16 Speech Understanding: PerceptionMachine 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.Institute of Computing
17 RoboticsAlthough 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).Institute of Computing
18 Natural Language Understanding 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.Institute of Computing
19 Planning 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.Institute of Computing
20 Expert SystemsExpert 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.Institute of Computing
21 Theorem ProvingProving 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.Institute of Computing
22 Symbolic MathematicsSymbolic mathematics refers to manipulation of formulas, rather than arithmetic on numeric values.AlgebraDifferential and Integral CalculusSymbolic 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)))Institute of Computing
23 Game PlayingGames 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.Institute of Computing
24 Characteristics of A.I. Programs 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.Institute of Computing
25 Symbolic ProcessingMost 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?Institute of Computing
26 Knowledge Representation It 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.Institute of Computing
28 Representation Systems What 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
29 Representation of Different representations of the real number π.
30 Block World Representation A blocks worldLogical Clauses describing some important properties and relationshipsGeneral rule
31 Bluebird Representations Logical predicates representing a simple description of a bluebird.Semantic network description of a bluebird.
33 State Space Search State space State space search State – any current representation of a problemAll possible state of the problemStart states – the initial state of the problemTarget states – the final states of the problem that has been solvedState space graphNodes – 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 existsDepth-firstBreath-firstBest-first searchheuristics
34 Tic-tac-toe State Space Portion of the state space for tic-tac-toe.
35 Auto Diagnosis State Space State space description of the automotive diagnosis problem.
36 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. ?