Intelligent Systems Introduction – Lecture 1 Prof. Dieter Fensel (& Francois Scharffe)
Agenda Overview of the lecture Introduction to the lecture topics
Overview Course home page: http://teaching-wiki.sti2.at/index.php/Intelligent_Systems (schedule, lecture notes, exercises, etc). Textbook: S. Russell and P. NorvigArtificial Intelligence: A Modern Approach Prentice Hall, 2003, Second Edition Lecturer: Dieter Fensel (email@example.com) Tutors: Gulay Unel Each week: lecture – tutorials organization come here Attendance of the tutorials is obligatory ! Miss at most 2 times with written excuse ("was such nice weather" doesn't count!) Grades: Lecture: final exam determines your grade Tutorials: Proper presentation of the reports and active interaction improves your grade. Reports should be submitted before the tutorial (more Details in the tutorials) …Questions?
Overview 1 Introduction 2 Propositional Logic 3 Predicate Logic 4 Model checking and theorem proving 5 Search methods 6 CommonKADS 7 Problem-Solving Methods 8 Planning 9 Agents 10 Rule learning 11 Inductive Logic Programming 12 Formal Concept Analysis 13 Neural networks 14 Semantic Web and Exam Preparation
What is AI? Views of AI fall into four categories: Thinking humanly Thinking rationally Acting humanly Acting rationally The textbook advocates "acting rationally"
Acting Humanly: Turing Test Turing (1950) "Computing machinery and intelligence": "Can machines think?" "Can machines behave intelligently?" Operational test for intelligent behavior: the Imitation Game Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes Anticipated all major arguments against AI in following 50 years Suggested major components of AI: knowledge, reasoning, language understanding, learning
Thinking Humanly: Cognitive Modelling 1960s "cognitive revolution": information-processing psychology Requires scientific theories of internal activities of the brain How to validate? Requires Predicting and testing behavior of human subjects (top-down) or Direct identification from neurological data (bottom-up) Both approaches (roughly, Cognitive Science and Cognitive Neuroscience) are now distinct from AI
Thinking Rationally: Laws of Thought Aristotle(384-322 BC) What are correct arguments/thought processes? Several Greek schools developed various forms of logic: notation and rules of derivation for thoughts; may or may not have proceeded to the idea of mechanization Direct line through mathematics and philosophy to modern AI Problems: Not all intelligent behavior is mediated by purely logical deliberation What is the purpose of thinking? What thoughts should I have?
Acting Rationally: Rational Agent Rational behavior: doing the "right" thing The right thing: that which is expected to maximize goal achievement, given the available information. Doesn't necessarily involve thinking – e.g., blinking reflex – but thinking should be in the service of rational action "Thinking" Declarative Problem Solving "Reflex" "hard-coded" solution algorithm
Rational Agents An agent is an entity that perceives and acts This course is in some sense about designing rational agents Abstractly, an agent is a function from percept histories to actions: [f: P*A] For any given class of environments and tasks, we seek the agent (or class of agents) with the best performance BUT: computational limitations make perfect rationality unachievable design best program for given machine resources.
What is AI for John McCarthy? Q. What is artificial intelligence? A. 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. Q. Yes, but what is intelligence? A. 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. ....[http://www-formal.stanford.edu/jmc/whatisai.html]
What is AI for Marvin Minsky? “Artificial Intelligence is the science of making machines do things that require intelligence if done by men.” Minsky and John McCarthy co-founded the MIT Artificial Intelligence Laboratory in 1961
Types of AI Symbolic AI: Symbolic AI is based on logic. It uses sequences of rules to tell the computer what to do next. Expert systems consist of many so-called IF-THEN rules: IF this is the case, THEN do that. Since both sides of the rule can be defined in complex ways, rule-based programs can be very powerful. Connectionist AI: Connectionism is inspired by the brain. It is closely related to computational neuroscience, which models actual brain cells and neural circuits. Connectionist AI uses artificial neural networks made of many units working in parallel. Each unit is connected to its neighbours by links that can raise or lower the likelihood that the neighbour unit will fire (excitatory and inhibitory connections respectively). Neural networks that are able to learn do so by changing the strengths of these links, depending on past experience. These simple units are much less complex than real neurons. Each can do only one thing: for instance, report a tiny vertical line at a particular place in an image. What matters is not what any individual unit is doing, but the overall activity-pattern of the whole network. Evolutionary AI: Evolutionary AI draws on biology. Its programs make random changes in their own rules, and select the best daughter programs to breed the next generation. This method develops problem-solving programs, and can evolve the “brains” and “eyes” of robots. It is often used in modelling artificial life (A-Life). A-Life studies self-organization: how order arises from something that is ordered to a lesser degree. Biological examples include the flocking patterns of birds and the development of embryos.
Branches of AI (John McCarthy) (1) Logical AI: What a program knows about the world in general the facts of the specific situation in which it must act, and its goals are all represented by sentences of some mathematical logical language. The program decides what to do by inferring that certain actions are appropriate for achieving its goals. Search:AI programs often examine large numbers of possibilities, e.g. moves in a chess game or inferences by a theorem proving program. Discoveries are continually made about how to do this more efficiently in various domains. Pattern recognition: When a program makes observations of some kind, it is often programmed to compare what it sees with a pattern. For example, a vision program may try to match a pattern of eyes and a nose in a scene in order to find a face. More complex patterns, e.g. in a natural language text, in a chess position, or in the history of some event are also studied. These more complex patterns require quite different methods than do the simple patterns that have been studied the most.
Branches of AI (John McCarthy) (2) Representation: Facts about the world have to be represented in some way. Usually languages of mathematical logic are used. Inference: From some facts, others can be inferred. Mathematical logical deduction is adequate for some purposes, but new methods of non-monotonic inference have been added to logic since the 1970s. The simplest kind of non-monotonic reasoning is default reasoning in which a conclusion is to be inferred by default, but the conclusion can be withdrawn if there is evidence to the contrary. Ordinary logical reasoning is monotonic in that the set of conclusions that can the drawn from a set of premises is a monotonic increasing function of the premises. Common sense knowledge and reasoning: This is the area in which AI is farthest from human-level, in spite of the fact that it has been an active research area since the 1950s. While there has been considerable progress, e.g. in developing systems of non-monotonic reasoning and theories of action, yet more new ideas are needed. The Cyc system contains a large but spotty collection of common sense facts.
Branches of AI (John McCarthy) (3) Learning from experience: Programs do that. The approaches to AI based on connectionism and neural nets specialize in that. There is also learning of laws expressed in logic. Programs can only learn what facts or behaviors their formalisms can represent, and unfortunately learning systems are almost all based on very limited abilities to represent information. Planning: Planning programs start with general facts about the world (especially facts about the effects of actions), facts about the particular situation and a statement of a goal. From these, they generate a strategy for achieving the goal. In the most common cases, the strategy is just a sequence of actions. Epistemology: This is a study of the kinds of knowledge that are required for solving problems in the world.
Branches of AI (John McCarthy) (4) Ontology: Ontology is the study of the kinds of things that exist. In AI, the programs and sentences deal with various kinds of objects, and we study what these kinds are and what their basic properties are. Emphasis on ontology begins in the 1990s. Heuristics: A heuristic is a way of trying to discover something or an idea imbedded in a program. The term is used variously in AI. Heuristic functions are used in some approaches to search to measure how far a node in a search tree seems to be from a goal. Heuristic predicates that compare two nodes in a search tree to see if one is better than the other, i.e. constitutes an advance toward the goal, may be more useful. Genetic programming: Genetic programming is a technique for getting programs to solve a task by mating random Lisp programs and selecting fittest in millions of generations.
AI prehistory 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 Economics utility, decision theory Neuroscience physical substrate for mental activity Psychology phenomena of perception and motor control, experimental techniques Computer building fast computers engineering Control theory design systems that maximize an objective function over time Linguistics knowledge representation, grammar
Abridged history of AI 1943 McCulloch & Pitts: Boolean circuit model of brain 1950 Turing's "Computing Machinery and Intelligence" 1956 Dartmouth meeting: "Artificial Intelligence" adopted 1952—69 Look, Ma, no hands! 1950s Early AI programs, including Samuel's checkers program, Newell & Simon's Logic Theorist, Gelernter's Geometry Engine 1965 Robinson's complete algorithm for logical reasoning 1966—73 AI discovers computational complexity Neural network research almost disappears 1969—79 Early development of knowledge-based systems 1980-- AI becomes an industry 1986-- Neural networks return to popularity 1987-- AI becomes a science 1995-- The emergence of intelligent agents
State of the Art Deep Blue defeated the reigning world chess champion Garry Kasparov in 1997 (http://www.research.ibm.com/deepblue/home/html/b.shtml) Automated Reasoning methods proved a mathematical conjecture (Robbins conjecture) unsolved for decades No hands across America (driving autonomously 98% of the time from Pittsburgh to San Diego) Military is still the strongest factor for AI research: During the 1991 Gulf War, US forces deployed an AI logistics planning and scheduling program that involved up to 50,000 vehicles, cargo, and people NASA's on-board autonomous planning program controlled the scheduling of operations for a spacecraft Proverb solves crossword puzzles better than most humans
AI around us ABS uses fuzzy logic (http://support.intel.com/design/mcs96/designex/2351.htm) Group of elevators work together under planning techniques to minimize the time that the users have to wait. (http://www.informatik.uni-freiburg.de/~koehler/elev/elev.html) Video Games like Age of Empire or Commandos use sophisticated AI techniques (neural networks, planning techniques, genetic algorithms, etc.) to increase the difficulty and realism of the game (http://www.itee.uq.edu.au/~penny/commercial_AI.htm).
Some AI relevant People Isaac Asimov * (http://www.asimovonline.com/) Arthur C. Clark * (http://www.clarkefoundation.org/) John McCarthy (http://www-formal.stanford.edu/jmc/) Marvin Minsky (http://web.media.mit.edu/~minsky/) Donald Michie (http://www.aiai.ed.ac.uk/~dm/dm.html) Allen Newell* (http://www.princeton.edu/~hos/frs122/newellobit.html) Herbert A. Simon* (http://www.psy.cmu.edu/psy/faculty/hsimon/hsimon.html) Alan Turing* (http://www.turing.org.uk/turing/)
References S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach Prentice Hall, 2003, Second Edition Alan Turing, Computing Machinery and Intelligence, 1950 J. Mac Carthy, Programs with Common Sense, 1959 A. Newell, The Knowledge Level, 1981 H. Simon, A Behavioral Model of Rational Choice, 1955 D. Fensel, E.Motta, Structured Development of Problem Solving Methods, 2001 T. Berners-Lee, J.Hendler, O. Lassila, The Semantic Web, 2001