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Artificial Intelligence

Nov 18, 2014





Chapter1 Anurag Dixit

1. Introduction What is artificial intelligence? It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable. What is intelligence? Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines. Natural Intelligence Definition. Intelligence inter ligare (Latin) the capacity of creating connections between notions. Wikipedia: the ability to solve problems. WordNet: the ability to comprehend; to understand and profit from experience. Complex use of creativity, talent, imagination. Biology - Intelligence is the ability to adapt to new conditions and to successfully cope with life situations. Psychology - a general term encompassing various mental abilities, including the ability to remember and use what one has learned, in order to solve problems, adapt to new situations, and understand and manipulate ones reality. Nonlinear, non-predictable behavior.


Artificial Intelligence Dictionary: Intelligence 1. (a) The capacity to acquire and apply knowledge. (b) The faculty of thought and reason. (c) Superior powers of mind. 2. An intelligent, incorporeal being, especially an angel. 3. Information; news. 4. (a) Secret information, especially about an actual or potential enemy. (b) An agency, staff, or office employed in gathering such information. (c) Espionage agents, organizations, and activities considered as a group What is intelligence then? Fast thinking? Knowledge? Ability to pass as a human? Ability to reason logically? Ability to learn? Ability to perceive and act upon ones environment? Ability to play chess at grand-masters level? Why Study AI?

Chapter1 Anurag Dixit

AI helps computer scientists and engineers build more useful and user-friendly computers, Psychologists, linguists, and philosophers understand the principles that constitute what we call intelligence. AI is an interdisciplinary field of study. Many ideas and techniques now standard in CS (symbolic computation, time sharing, objects, declarative programming, . . . ) were pioneered by AI-related research.


Artificial Intelligence Dictionary: Artificial Intelligence 1. Dictionary 1:

Chapter1 Anurag Dixit

(a) The ability of a computer or other machine to perform those activities that are normally thought to require intelligence. (b) The branch of computer science concerned with the development of machines having this ability. 2. Dictionary 2: The subfield of computer science concerned with the concepts and methods of symbolic inference by computer and symbolic knowledge representation for use in making inferences. AI can be seen as an attempt to model aspects of human thought on computers. It is also sometimes defined as trying to solve by computer any problem that a human can solve faster. Definition from R & N book: a program that Acts like human (Turing test) Thinks like human (human-like patterns of thinking steps) Acts or thinks rationally (logically, correctly)

Definition: The science of developing methods to solve problems usually associated with human intelligence. Alternate definitions: building intelligent entities or agents; making computers think or behave like humans studying the human thinking through computational models; generating intelligent behavior, reasoning, learning.

Ray Kurzweil on AI Artificial intelligence is the ability to perform a task that is normally performed by natural intelligence, particularly human natural 3

Artificial Intelligence

Chapter1 Anurag Dixit

intelligence. (or in some cases, tasks that require greater-than-human intelligence) John McCarthy on AI It is the science and engineering of making intelligent machines, especially intelligent computer programs. Intelligence is the computational part of the ability to achieve goals in the world. Operational Definition of AI Systems that act like humans Turing test. Systems that think like humans Cognitive Science Systems that think rationally Logic-based AI Systems that act rationally Rational Agents Thinking Rationally: Laws of Thought Several Greek schools at the time of Aristotle developed various forms of logic: Notation and rules of derivation for thoughts; they may or may not have proceeded to the idea of mechanization Direct line through mathematics and philosophy to modern AI Problems: 1. Not all intelligent behavior is mediated by logical deliberation 2. What is the purpose of thinking? What thoughts should I have? Thinking Humanly: Cognitive Science 1960s cognitive revolution: information-processing psychology replaced prevailing orthodoxy of behaviorism


Artificial Intelligence Require scientific theories of internal activities of the brain What level of abstraction? Knowledge or circuits? How to validate? It requires 1. Predicting and testing behavior of human subjects (top-down) 2. Direct identification from neurological data (bottom-up)

Chapter1 Anurag Dixit

Both approaches, Cognitive Science and Cognitive Neuroscience, share with AI on: the available theories do not explain (or engender) anything resembling human-level general intelligence. Rational Agents An agent is an entity that perceives and acts This course is about designing rational agents Abstractly, an agent is a function from percept histories to actions: For any given class of environments and tasks, we seek the agent (or class of agents) with the best performance Caveat: computational limitations make perfect rationality unachievable Approach: design best program for given machine resources Acting Rationally Rational behavior: doing the right thing, that which is expected to maximize goal achievement, given the available information Doesnt necessarily involve thinkinge.g., blinking reflexbut thinking should be in the service of rational action Aristotle: Every art and every inquiry, and similarly every action and pursuit, is thought to aim at some good.

Artificial Intelligence Vs Conventional Programming


Artificial Intelligence

Chapter1 Anurag Dixit

Artificial IntelligenceArtificial Intelligence a. primarily symbolic b. heuristic search (solution steps implicit) c. control structure usually separate from domain knowledge d. usually easy to modify, update and enlarge e. some incorrect answers often tolerable f. satisfactory answers usually acceptable

Conventional ProgrammingConventional computer programming a'. algorithmic (solutions steps explicit) b'. primarily numeric c'. information and control integrated together d'. difficult to modify e'. correct answers required f'. best possible solution usually sought

Foundations of AI (a) Computer Science & Engineering Computer hardware and Software (b) Philosophy Rule of Reasoning (c) Biology Human /animals brain activity (d) Linguistics communication (e) Cognitive Science High level human/animal thinking (f) Psychology Complex systems game (g) Economics Cost benefits ratio (h) Mathematics


Artificial Intelligence Logic algorithm optimization AI Prehistory

Chapter1 Anurag Dixit

Philosophy logic, methods of reasoning mind as physical system foundations of learning, language, rationality Mathematics formal representation and proof algorithms, computation, (un)decidability, (in)tractability probability Psychology adaptation phenomena of perception and motor control experimental techniques (psychophysics, etc.) Economics formal theory of rational decisions Linguistics knowledge representation grammar Neuroscience plastic physical substrate for mental activity Control theory homeostatic systems, stability simple optimal agent designs History of AI The birth of AI (1943 1956) Pitts and McCulloch (1943): simplified mathematical model of neurons (resting/firing states) can realize all propositional logic primitives (can compute all Turing computable functions) Allen Turing: Turing machine and Turing test (1950) Claude Shannon: information theory; early game theory, possibility of chess playing computers Tracing back to Boole, Aristotle, Euclid (logics, syllogisms, algebra of symbols) Early enthusiasm (1952 1969) 1956 Dartmouth conference

John McCarthy (Lisp); Marvin Minsky (first neural network machine); 7

Artificial Intelligence Alan Newell and Herbert Simon (GPS); Emphasize on intelligent general problem solving

Chapter1 Anurag Dixit

Heuristics of human problem solving (means-ends analysis in GPS ); Resolution by John Robinson (basis for automatic theorem proving); heuristic search (A*, AO*, game tree search) Emphasis on knowledge (1966 1974) domain specific knowledge is the key to overcome existing difficulties knowledge representation (KR) paradigms declarative vs. procedural representation

Knowledge-based systems (1969 1979) DENDRAL: the first knowledge intensive system (determining 3D structures of complex chemical compounds) MYCIN: first rule-based expert system (containing 450 rules for diagnosing blood infectious diseases) EMYCIN: an ES shell PROSPECTOR: first knowledge-based system that made significant profit (geological ES for mineral deposits)

AI became an industry (1980 1989) wide applications in various domains commercially available tools

Current trends (1990 present) more realistic goals more practical (application oriented) resurgence of neural networks and emergence of genetic algorithms


Artificial Intelligence distributed AI, intelligent agents, and semantic web