Artificial intelligent Lec 1-ai-introduction-
- 2. Artificial Intelligence Course No.: CSC 343 Lect.: 3 h Lab. : 2 h Marks: 65 final 10 Y. work 25 Lab+ Oral Exam hours: 3 h By Prof. Dr. : Taymoor M. Nazmy
- 3. The text book Russell & Norvig, "Artificial Intelligence: A Modern Approach", 2nd Edition, Prentice Hall, 2003.
- 4. Course Objective A broad introduction and appreciation of Artificial Intelligence and its applications. Course Outline Definition of Artificial Intelligence. Modelling intelligence: adaptive and knowledge based approaches to analysis and design of intelligent systems. Application domains of Artificial Intelligence. Types of Artificial Intelligence systems.
- 5. Course Content Introduction Rational Agents Search and Problem solving Informed search Propositional logic Predicate logic Knowledge representation using logic Planning Probabilistic Reasoning Planning under uncertainty Machine Learning Reinforcement learning Neural Networks Natural Language Processing
- 7. What is Artificial Intelligence? definitions Turing test rational thinking acting rationally Foundations of Artificial Intelligence philosophy mathematics psychology computer science linguistics History of Artificial Intelligence Applications of AI
- 8. -select a task that you believe requires intelligence examples: -playing chess, solving puzzles, translating from English to German, finding a proof for a theorem. -for that task, sketch a computer-based system that tries to solve the task architecture, components, behavior. what are the computational methods your system relies on- e.g. data bases, matrix multiplication, graph traversal. what are the main challenges? how do humans tackle the task?
- 9. Foundations of AI Computer Science & Engineering AI Mathematics Cognitive Science Philosophy Psychology Linguistics BiologyEconomics
- 10. Foundations of AI Engineering: robotics, vision, control-expert systems, biometrics, Computer Science: AI-languages , knowledge representation, algorithms, … Pure Sciences: statistics approaches, neural nets, fuzzy logic, … Linguistics: computational linguistics, phonetics , speech, … Psychology: cognitive models, knowledge-extraction from experts, … Medicine: human neural models, neuroscience,...
- 11. Fundamental Issues for most AI problems 1- Representation Facts about the world have to be represented in some way, e.g., mathematical logic is one language that is used in AI. Deals with the questions of what to represent and how to represent it. How to structure knowledge? What is explicit, and what must be inferred? How to encode "rules" for inferencing so as to find information that is only implicitly known? How to deal with incomplete, inconsistent, and probabilistic knowledge?
- 12. 2-Search Many tasks can be viewed as searching a very large problem space for a solution. For example, Checkers has about 1040 states, and Chess has about 10120 states in a typical games. Use of heuristics (meaning "serving to aid discovery") and constraints. 3-Inference From some facts others can be inferred. Related to search. For example, knowing "All elephants have trunks" and "Clyde is an elephant," can we answer the question "Does Clyde have a trunk?" Deduction, abduction, reasoning under uncertainty.
- 13. 4-Learning Inductive inference, neural networks, genetic algorithms, evolutionary approaches. 5-Planning Starting with general facts about the world, facts about the effects of basic actions, facts about a particular situation, and a statement of a goal, generate a strategy for achieving that goals in terms of a sequence of primitive steps or actions.
- 14. “AI develops programming paradigms, languages, tools, and environments for application areas for which conventional programming fails”, such as: Symbolic programming (LISP) Logical Programming (PROLOG) Rule-based Programming (Expert system shells) Soft Computing (Belief network tools, fuzzy logic tool boxes,…) Object-oriented programming (Smalltalk)
- 15. What is AI?
- 16. What is AI? Many definitions, most fit into one of four categories: Systems that act humanly Systems that think humanly Systems that act rationally Systems that think rationally
- 17. Systems that think like humans Cognitive science Fascinating area, but we will not be covering it in this course. Systems that think rationally Aristotle: What are the correct thought processes Systems that reason in a logical manner Systems doing inference correctly. Systems that act rationally (Rational behavior ) Doing the right thing Rational agent approach Agent: entity that perceives and acts Rational agent: acts so to achieve best outcome
- 18. Turing in 1950 published a philosophical paper designed to stop people arguing about whether or not machines could think. He proposed that the question be replaced with a test.
- 19. Acting Rationally Acting rationally means that one acts to achieve his/her goals given his/her beliefs. AI can be viewed as the study and creation of rational agents. An agent is something that can perceive and act. The study of AI as rational agent design has two advantages: (a) it is more general than the “laws of thought” approach, and (b) it is more amenable to scientific development than approaches that limit themselves to human behavior or human thought.
- 24. History
- 25. Examples of Modeling Human Intelligence Semantic networks are designed after the psychological model of the human associative memory. John Plumber Person Owner Ford Car May 97 Time Oct 00 Ownership Situation Is a Is a Is a Is a Is a Is a Owner Ownee Start-time End-time Ford Is a
- 26. Modeling Human Intelligence Rule-based or Expert systems - Knowledge bases consisting of hundreds or thousands of rules of the form: IF (condition) THEN (action). Use rules to store knowledge (“rule-based”). The rules are usually gathered from experts in the field being represented (“expert system”). Most widely used knowledge model in the commercial world. IF (it is raining AND you must go outside) THEN (put on your raincoat) Rules can fire off a chain of other rules IF (raincoat is on) THEN (will not get wet)
- 27. Expert Systems Expert systems were commercially the most successful domain in Artificial Intelligence. Somewhat out of favor today These programs mimic the experts in whatever field. Auto mechanic Telephone networking Cardiologist Delivery routing Organic compounds Professional auditor Mineral prospecting Manufacturing Infectious diseases Pulmonary function Diagnostic internal medicine Weather forecasting VAX computer configuration Battlefield tactician Engineering structural analysis Space-station life support Audiologist Civil law
- 28. Expert Systems Two major parts of an expert system: The knowledge base: The collection of rules that make up the expert system. The inference engine: A program that uses the rules by making several passes over them. On each pass, the inference engine looks for all rules whose condition is satisfied (if part). It then takes the action (then part) and makes another pass over all the rules looking for matching condition. This goes on until no rules’ conditions are matched. The results are all those action parts left.
- 29. Human Brain and Neural Networks -Human Brain is made up of Billions of cells called neurons, - Neurons work when grouped together Decisions are made by passing electrical signals, - Neurons are devices for processing Binary digits, -A neuron: many-inputs / one-output unit, -output can be excited or not excited, -incoming signals from other neurons determine if the neuron shall excite ("fire") Output subject to attenuation in the synapses, which are junction parts of the neuron
- 30. Artificial Neural Network: A collection of neurons which are interconnected. The output of one connects to several others with different strength connections. Initially, neural networks have no knowledge. (All information is learned from experience using the network.) Input 1 Input 2 Input 3 Neuron 1 Neuron 2 Output from Neuron 1 Output from Neuron 2 Artificial neural network Natural NN
- 31. Comparing the computer with the human brain
- 32. Evolutionary Systems Genetic Programming: A technique that follows Darwinian evolution. The evolution takes place directly on the programs in the population that are striving to reach the goal specified by the programmer. Only the goal is known and possibly some of the structure of the solution..
- 33. The concept of modern approach of AI (Perception and action AI agent) Organisms in the real world have gather information about their environment (perception) and based on this information, they have to manipulate their environment (including themselves) in a way that is advantageous to them (action). The action in turn may cause a change in the organism’s perception, which can lead to a different type of action. We call this the perception-action cycle. Complex organisms do not just perceive and act, but they also have an internal state that changes based on the success of previous perception-action cycles. This is the mechanism of learning.
- 34. AI “Application” Areas Rule-Based Expert Systems Medical Diagnosis: MYCIN, INTERNIST, PUFF CSP Scheduling: ISIS, Airline scheduling Data Mining Financial: Fraud detection, credit scoring Sales: Customer preferences, inventory Science: NASA galaxy DB, genome analysis
- 35. AI “Application” Areas (cont.) Language Processing Speech: dictation, HCI Language: Machine Translation ML & NLP: Fact Extraction ML & words: Information Retrieval Robotics Machine Vision Mobile Robots & “agents” Manipulation
- 36. Applications Game playing : Chess, Draughts,.. Speech recognition :speech to word processors Understand natural language: understand meaning of a whole sentence Computer vision : 3-D world, but human eye and camera are 2 Expert systems: stored knowledge Robotics: space missions Character recognition :handwriting Pattern recognition: Faces, fingerprint,