ARTIFICIAL INTELLIGENCE

Structure and Strategies for Complex Problem Solving Author: George F Luger and William Stebblfield Edition:Third Publisher:Addison Wisely

INTRODUCTION  Artificial Intelligence (AI) is the design and study of computer programs that behave intelligently.  These programs are constructed to perform as humans or as animals whose behavior we consider intelligent.  AI researchers have written programs that control nuclear power plants and diagnose problems in complicated electronic devices.  AI is concerned with programs that respond flexibly in situations that were not specifically anticipated by the programmer. A house cleaning robot should distinguish between a scrap of tin foil and a diamond ring. A face recognition system be able to identify the same face with a different hat or hair cut.

Intelligence … Intelligence is defined as: –The faculty of understanding; –Capacity for learning, reasoning, and understanding; –Aptitude in grasping truths, relationships, facts, meaning, etc. –Mental alertness or quickness of understanding; and –Manifestation of a high mental capacity.

Artificial Intelligence... The most common definition of Artificial Intelligence is: –The study of how to make computers do things which, at the moment, people do better (Rich, 1991). Another way of defining AI is: –The area of computer science focusing on creating machines that can engage on behaviour that humans consider intelligent.

Definition... AI is a discipline that allows us to build intelligent computers (Systems/applications) which are capable of learning, understanding, and developing a sense to forecast, foretell, and foresee the behaviour (Ali 1987).

Branches of AI … AI Expert Systems Ontology Heuristics Pattern Recognition Representation Inference Learning from Experience Search Logical AI Planning

 Much of the early work in AI focused on formal tasks, such as game playing and theorem proving.  Game playing and theorem proving share the property that people who do them well are considered to be displaying intelligence.  AI is the part of computer science concerned with designing intelligent computer systems, that is, computer systems that exhibit the characteristics we associate with intelligence in human behaviour - understanding language, learning, reasoning and solving problems.  The “Logic Theorist” was an early attempt to prove mathematical theorems. It was able to prove several theorems from the first chapter of a famous book of Whitehead and Russell “Principia Mathematica”.  Another mathematician Gelernter explored another area of mathematics for writing a theorem proving program e.g. geometry.

 Newell and Simon explored yet another area called “common sense reasoning”. This area covers:-  Problem solving that we do every day when we decide how to get to work in the morning.  As well as actions and their consequences.  To investigate this sort of reasoning they wrote a program called “General Problem Solver”(GPS).

 AI research progressed and techniques for handling larger amounts of world knowledge were developed. Work also focused on following areas:-  Perception (vision and speech).  Natural Language Understanding.  Problem solving in medical diagnosis  Problem solving in chemical analysis.  Engineering Design  Scientific Discovery  Financial Planning

 The problem areas where AI is now flourishing most as a practical discipline are primarily the domains that require only specialized expertise without the assistance of commonsense knowledge. These are called “ Expert Systems”.  There are now thousands of Expert Systems in day-to- day operation throughout all areas of industry and government around the world.  Each of these systems attempts to solve part of or all of a practical significant problem that previously required human expertise.

A FEW DOMAINS OF AI Mundane Tasks  Perception  Vision  Speech  Natural Language  Understanding  Generation  Translation  Common sense Reasoning  Robot Control

Formal Tasks  Games  Tic-Tac-Toe  Checkers  Chess  Other Games  Mathematics  Geometry  Logic  Integrated Calculus  Theorem Proving A FEW DOMAINS OF AI

Expert Tasks  Engineering  Design  Fault finding  Manufacturing planning  Scientific analysis  Medical diagnosis  Financial analysis A FEW DOMAINS OF AI

Knowledge  One of the few hard and fast results to come out of the first three decades of AI research is that intelligence requires knowledge. There are a few undesirable properties of knowledge:-  It is voluminous.  It is hard to characterize accurately.  It is constantly changing.  It differs from data by being organized in a way that corresponds to the ways it will be used.

 AI Technique is a method that exploits knowledge that should be represented in such a way that:-  The knowledge captures generalizations, i.e. it is not necessary to represent separately each individual situation. Instead situations that share important properties are grouped together. If knowledge does not have this property, inordinate amounts of memory and updates will be required. So we usually call something without this property “Data” rather than knowledge.  In many AI domains, most of the “knowledge” a program has must ultimately be provided by people in terms they understand. Though in few AI applications, the “data” can be provided automatically by taking readings from a variety of instruments. AI Technique

 AI Technique is a method that exploits knowledge that should be represented in such a way that:-  It can be easily modified to correct errors and to reflect changes in the world and in our world view.  It can be used in great many situations even if it is not totally accurate or complete.  It can be used to help overcome its own sheer bulk by helping to narrow the range of possibilities that must usually be considered.

Artificial Intelligence - Theory  AI is more than an engineering discipline, it is also a subject of scientific investigation.  Researchers construct theories about what AI programs are capable of and test them with mathematical analysis or experiments.  Theories are subjected to examination analytically by developing mathematical abstractions and proving theorems.  They are also studied empirically by developing programs, running experiments, and analyzing the results.  The behavior of complex AI systems is difficult to predict. Often researchers are surprised by the behavior of the system that they build.

Inferring Structure from Motion in Machine Vision. Machine vision is concerned with interpreting the information contain in electronic camera images. Research has proved that this information can be used to answer questions about the structure and motion of the object captured in those images. Finding Consistent Hypothesis in Learning. In concept learning, a system is given a set of examples of a target concept and asked to find a hypothesis describing the concept that is consistent with examples it has seen so far. Probabilistic Inference in Diagnostic Reasoning. In medical diagnosis, networks involving probabilities are used to infer the most likely disease from a patient’s symptoms. Examples of Artificial Intelligence - Theory

Search in Automated Planning. In planning for robots or factories, it is desirable that an algorithm never consider the same plan twice and that the algorithm always find a solution to a planning problem if one exists. Parsing Sentences in Language Understanding. Parsing reveals the structure of sentences and is an important step in automated language understanding. It is difficult to program a computer to understand context of a spoken or written sentence. Therefore, for context free languages, parsing sentences is computationally easy. Though most human languages are not context free languages. Examples of Artificial Intelligence - Theory

Artificial Intelligence in Practice  AI systems serve a wide variety of practical purposes. There are programs that generate investment strategies by predicting trends in the stock market, diagnose patient illnesses suggesting treatment, and control assembly robots in factories.  People who work in AI consider themselves to be engineers. They build practical tools. These tools called AI systems are used to plan routes for airlines, build cars in factories, and also play master level chess.

 Game Playing  Automated Reasoning and Theorem Proving  Expert Systems  Natural Language Understanding and Semantic Modeling  Modeling Human Performance  Planning and Robotics  Languages and Environments for AI  Machine Learning Examples of Artificial Intelligence Systems

Alan Turing, a pioneer in the theory of computation, proposed an intelligence test for computer programs. A human judge is allowed to interrogate a program through a video terminal and a human simultaneously. If the program can fool the judge into believing that it is another human responding rather than a computer, then the program is judged to be intelligent. One can imagine variants of this test in which:- –One manipulates a robot’s environment to see how the robot responds and judge the robot as intelligent or not in comparison with the responses of a human worker under similar conditions. Turing Test The Measurement of Intelligence of AI Systems

Artificial Intelligence - Summary It has been attempted to define artificial intelligence through discussion of its major areas of research and application. The primary concern of AI is to find effective way to understand and apply intelligent problem solving, planning and communication skills to wide range of practical problems. In spite of the variety of problems addressed in Artificial Intelligence research, a number of important features emerge that seem common to all divisions of the field, these are summarized as under:-  The use of computers to do reasoning, pattern recognition, learning, or some other form of inference.  A focus on problems that do not respond to algorithmic solutions. This underlies the reliance on heuristic search as an AI problem- solving technique.

 A concern with problem solving using inexact, missing, or poorly defined information and the use of representational formalisms that enable the programmer to compensate for these problems.  Reasoning about the significant qualitative features of a situation.  An attempt to deal with issues of semantic meaning as well as syntactic form.  Answers that are neither exact nor optimal, but are in some sense “sufficient.” This is a result of the essential reliance on heuristic problem- solving methods in situations where optimal or exact results are either too expensive or not possible.  The use of large amounts of domain-specific knowledge in solving problems. This is the basis of expert systems. Artificial Intelligence - Summary

Assignment Get the code of any games Write down the worst effect of AI on our society