* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Artificial Intelligence - KDD
Survey
Document related concepts
Knowledge representation and reasoning wikipedia , lookup
Pattern recognition wikipedia , lookup
Concept learning wikipedia , lookup
Collaborative information seeking wikipedia , lookup
Technological singularity wikipedia , lookup
Machine learning wikipedia , lookup
Embodied cognitive science wikipedia , lookup
Ethics of artificial intelligence wikipedia , lookup
Intelligence explosion wikipedia , lookup
History of artificial intelligence wikipedia , lookup
Philosophy of artificial intelligence wikipedia , lookup
Existential risk from artificial general intelligence wikipedia , lookup
Transcript
Lecture 1 Artificial Intelligence: Foundations Review Wednesday, January 17, 2001 William H. Hsu Department of Computing and Information Sciences, KSU http://www.cis.ksu.edu/~bhsu Reading for Next Class: Chapter 2, Russell and Norvig Handout, Artificial Intelligence (3e), P. H. Winston Handout, Essentials of Artificial Intelligence, M. Ginsberg CIS 830: Advanced Topics in Artificial Intelligence Kansas State University Department of Computing and Information Sciences Course Outline • Overview: Intelligent Systems and Applications • Artificial Intelligence (AI) Software Development Topics – Knowledge representation • Logical • Probabilistic – Search • Problem solving by (heuristic) state space search • Game tree search – Planning: classical, universal – Machine learning • Models (decision trees, version spaces, ANNs, genetic programming) • Applications: pattern recognition, planning, data mining and decision support – Topics in applied AI • Computer vision fundamentals • Natural language processing (NLP) and language learning survey • Practicum (Short Software Implementation Project) CIS 830: Advanced Topics in Artificial Intelligence Kansas State University Department of Computing and Information Sciences Questions Addressed • Problem Area – What are intelligent systems and agents? – Why are we interested in developing them? • Methodologies – What kind of software is involved? What kind of math? – How do we develop it (software, repertoire of techniques)? – Who uses AI? (Who are practitioners in academia, industry, government?) • Artificial Intelligence as A Science – What is AI? – What does it have to do with intelligence? Learning? Problem solving? – What are some interesting problems to which intelligent systems can be applied? – Should I be interested in AI (and if so, why)? • Today: Brief Tour of AI History – Study of intelligence (classical age to present), AI systems (1940-present) – Viewpoints: philosophy, math, psychology, engineering, linguistics CIS 830: Advanced Topics in Artificial Intelligence Kansas State University Department of Computing and Information Sciences What is AI? [1] • Four Categories of Systemic Definitions – – – – • 1. Think like humans 2. Act like humans 3. Think rationally 4. Act rationally Thinking Like Humans – Machines with minds (Haugeland, 1985) – Automation of “decision making, problem solving, learning…” (Bellman, 1978) • Acting Like Humans – Functions that require intelligence when performed by people (Kurzweil, 1990) – Making computers do things people currently do better (Rich and Knight, 1991) • Thinking Rationally – Computational models of mental faculties (Charniak and McDermott, 1985) – Computations that make it possible to perceive, reason, and act (Winston, 1992) • Acting Rationally – Explaining, emulating intelligent behavior via computation (Schalkoff, 1990) – Branch of CS concerned with automation of intelligent behavior (Luger and Stubblefield, 1993) CIS 830: Advanced Topics in Artificial Intelligence Kansas State University Department of Computing and Information Sciences What is AI? [2] Thinking and Acting Like Humans • Concerns: Human Performance (Figure 1.1 R&N, Left-Hand Side) – Top: thought processes and reasoning (learning and inference) – Bottom: behavior (interacting with environment) • Machines With Minds – Cognitive modelling • Early historical examples: problem solvers (see R&N Section 1.1) • Application (and one driving force) of cognitive science – Deeper questions • What is intelligence? • What is consciousness? • Acting Humanly: The Turing Test Approach – Capabilities required • Natural language processing • Knowledge representation • Automated reasoning • Machine learning – Turing Test: can a machine appear indistinguishable from a human to an experimenter? CIS 830: Advanced Topics in Artificial Intelligence Kansas State University Department of Computing and Information Sciences What is AI? [3] Viewpoints on Defining Intelligence • Genuine versus Illusory Intelligence – Can we tell? • If so, how? • If not, what limitations do we postulate? – The argument from disability (“a machine can never do X”) • Turing Test Specification – Objective: develop intelligent system “indistiguishable from human” • Blind interrogation scenario (no direct physical interaction – “teletype”) • 1 AI system, 1 human subject, 1 interrogator • Variant: total Turing Test (perceptual interaction: video, tactile interface) – Is this a reasonable test of intelligence? – Details: Section 26.3, R&N – See also: Loebner Prize page • Searle’s Chinese Room – Philosophical issue: is (human) intelligence a pure artifact of symbolic manipulation? – Details: Section 26.4, R&N – See also: consciousness in AI resources CIS 830: Advanced Topics in Artificial Intelligence Kansas State University Department of Computing and Information Sciences What is AI? [3] Thinking and Acting Rationally • Concerns: Human Performance (Figure 1.1 R&N, Right-Hand Side) – Top: thought processes and reasoning (learning and inference) – Bottom: behavior (interacting with environment) • Computational Cognitive Modelling – Rational ideal • In this course: rational agents • Advanced topics: learning, utility theory, decision theory – Basic mathematical, computational models • Decisions: automata (Chomsky hierarchy – FSA, PDA, LBA, Turing machine) • Search • Concept learning • Acting Rationally: The Rational Agent Approach – Rational action: acting to achieve one’s goals, given one’s beliefs – Agent: entity that perceives and acts – Focus of next lecture • “Laws of thought” approach to AI: correct inferences (reasoning) • Rationality not limited to correct inference CIS 830: Advanced Topics in Artificial Intelligence Kansas State University Department of Computing and Information Sciences Intelligent Agents: Overview • Agent: Definition – Any entity that perceives its environment through sensors and acts upon that environment through effectors – Examples (class discussion): human, robotic, software agents • Perception – Signal from environment – May exceed sensory capacity • Percepts Sensors – Acquires percepts – Possible limitations • Action Sensors ? Environment Agent – Attempts to affect environment – Usually exceeds effector capacity • Effectors Actions Effectors – Transmits actions – Possible limitations CIS 830: Advanced Topics in Artificial Intelligence Kansas State University Department of Computing and Information Sciences Application: Knowledge Discovery in Databases CIS 830: Advanced Topics in Artificial Intelligence Kansas State University Department of Computing and Information Sciences Rule and Decision Tree Learning • Example: Rule Acquisition from Historical Data • Data – Customer 103 (visit = 1): Age 23, Previous-Purchase: no, Marital-Status: single, Children: none, Annual-Income: 20000, Purchase-Interests: unknown, StoreCredit-Card: no, Homeowner: unknown – Customer 103 (visit = 2): Age 23, Previous-Purchase: no, Marital-Status: married, Children: none, Annual-Income: 20000: Purchase-Interests: car, Store-CreditCard: yes, Homeowner: no – Customer 103 (visit = n): Age 24, Previous-Purchase: yes, Marital-Status: married, Children: yes, Annual-Income: 75000, Purchase-Interests: television, Store-CreditCard: yes, Homeowner: no, Computer-Sales-Target: YES • Learned Rule – IF customer has made a previous purchase, AND customer has an annual income over $25000, AND customer is interested in buying home electronics THEN probability of computer sale is 0.5 – Training set: 26/41 = 0.634, test set: 12/20 = 0.600 – Typical application: target marketing CIS 830: Advanced Topics in Artificial Intelligence Kansas State University Department of Computing and Information Sciences Text Mining: Information Retrieval and Filtering • 20 USENET Newsgroups – comp.graphics misc.forsale soc.religion.christian sci.space – comp.os.ms-windows.misc rec.autos talk.politics.guns – comp.sys.ibm.pc.hardware rec.motorcycles talk.politics.mideast sci.electronics – comp.sys.mac.hardware rec.sports.baseball talk.politics.misc – comp.windows.x rec.sports.hockey talk.religion.misc – • sci.crypt sci.med alt.atheism Problem Definition [Joachims, 1996] – Given: 1000 training documents (posts) from each group – Return: classifier for new documents that identifies the group it belongs to • Example: Recent Article from comp.graphics.algorithms Hi all I'm writing an adaptive marching cube algorithm, which must deal with cracks. I got the vertices of the cracks in a list (one list per crack). Does there exist an algorithm to triangulate a concave polygon ? Or how can I bisect the polygon so, that I get a set of connected convex polygons. The cases of occuring polygons are these: ... • Performance of Newsweeder (Naïve Bayes): 89% Accuracy CIS 830: Advanced Topics in Artificial Intelligence Kansas State University Department of Computing and Information Sciences Specifying A Learning Problem • Learning = Improving with Experience at Some Task – Improve over task T, – with respect to performance measure P, – based on experience E. • Example: Learning to Filter Spam Articles – T: analyze USENET newsgroup posts – P: function of classification accuracy (discounted error function) – E: training corpus of labeled news files (e.g., annotated from Deja.com) • Refining the Problem Specification: Issues – What experience? – What exactly should be learned? – How shall it be represented? – What specific algorithm to learn it? • Defining the Problem Milieu – Performance element: How shall the results of learning be applied? – How shall the performance element be evaluated? The learning system? CIS 830: Advanced Topics in Artificial Intelligence Kansas State University Department of Computing and Information Sciences Survey of Machine Learning Methodologies • Supervised (Focus of CIS730) – What is learned? Classification function; other models – Inputs and outputs? Learning: examples x,f x approximat ion fˆx – How is it learned? Presentation of examples to learner (by teacher) – Projects: MLC++ and NCSA D2K; wrapper, clickstream mining applications • Unsupervised (Surveyed in CIS730) – Cluster definition, or vector quantization function (codebook) – Learning: observatio ns x distance metric d x , x discrete codebook f x 1 2 – Formation, segmentation, labeling of clusters based on observations, metric – Projects: NCSA D2K; info retrieval (IR), Bayesian network learning applications • Reinforcement (Not Emphasized in CIS730) – Control policy (function from states of the world to actions) – Learning: state/reward sequence si ,ri : 1 i n policy p : s a – (Delayed) feedback of reward values to agent based on actions selected; model updated based on reward, (partially) observable state CIS 830: Advanced Topics in Artificial Intelligence Kansas State University Department of Computing and Information Sciences Related Online Resources • Research – KSU Laboratory for Knowledge Discovery in Databases http://www.kddresearch.org (see especially Group Info, Web Resources) – KD Nuggets: http://www.kdnuggets.com • Courses and Tutorials Online – At KSU • CIS798 Machine Learning and Pattern Recognition http://www.kddresearch.org/Courses/Fall-1999/CIS798 • CIS730 Introduction to Artificial Intelligence http:// www.kddresearch.org/Courses/Fall-2000/CIS730 • CIS690 Implementation of High-Performance Data Mining Systems http:// www.kddresearch.org/Courses/Summer-2000/CIS690 – Other courses: see KD Nuggets, www.aaai.org, www.auai.org • Discussion Forums – Newsgroups: comp.ai.* – Recommended mailing lists: Data Mining, Uncertainty in AI – KSU KDD Lab Discussion Board: http://www.kddresearch.org/Board CIS 830: Advanced Topics in Artificial Intelligence Kansas State University Department of Computing and Information Sciences Terminology • Artificial Intelligence (AI) – Operational definition: study / development of systems capable of “thought processes” (reasoning, learning, problem solving) – Constructive definition: expressed in artifacts (design and implementation) • • Intelligent Agents Topics and Methodologies – Knowledge representation • Logical • Uncertain (probabilistic) • Other (rule-based, fuzzy, neural, genetic) – Search – Machine learning – Planning • Applications – – – – Problem solving, optimization, scheduling, design Decision support, data mining Natural language processing, conversational and information retrieval agents Pattern recognition and robot vision CIS 830: Advanced Topics in Artificial Intelligence Kansas State University Department of Computing and Information Sciences Summary Points • Artificial Intelligence: Conceptual Definitions and Dichotomies – Human cognitive modelling vs. rational inference – Cognition (thought processes) versus behavior (performance) – Some viewpoints on defining intelligence • Roles of Knowledge Representation, Search, Learning, Inference in AI – Necessity of KR, problem solving capabilities in intelligent agents – Ability to reason, learn • Applications and Automation Case Studies – – – – • Search: game-playing systems, problem solvers Planning, design, scheduling systems Control and optimization systems Machine learning: pattern recognition, data mining (business decision support) More Resources Online – Home page for AIMA (R&N) textbook – CMU AI repository – KSU KDD Lab (Hsu): http:// www.kddresearch.org – Comp.ai newsgroup (now moderated) CIS 830: Advanced Topics in Artificial Intelligence Kansas State University Department of Computing and Information Sciences