Artificial intelligence


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Artificial intelligence

  1. 1. 2013 Artificial Intelligent & Expert System Under supervision of Dr. Mahmoud Mostafa 4/7/2013
  2. 2. Points of view1) An Overview AI:2) Components of AI3) What is Artificial Intelligence?4) Characteristics of AI Systems5) Application of AI6) Overview ES.7) How Expert Systems Work8) Components of ES9) Knowledge Representation and the Knowledge Base10) Case-Based Reasoning11) Inference Engine12) Forward chaining13) Backward chaining 1
  3. 3. 14) Fuzzy Logic Systems15) Neural Networks16) Genetic Algorithms17) Hybrid AI Systems18) Intelligent Agents 2
  5. 5. 1. An Overview AI:Artificial Intelligence or AI is one of the most important fields of sciences, as new and moderntechnologies have become the most dominant thing in our world,the rapid development of computershas helped many researchers to accomplish many tasks and goals in their field with less time and lesscost also, so if you want to ask about something you want you won’t go anywhere and ask but you cancontact many people through internet and communicate and collaborate with them about yourproblem .so according to the developed computerized tools we arrive to that question why we won’tmake a machine that can behave and imitate human being in many things and also with the ability tolearn other things, according to Simon (1977): any individual in the entire world has only limitedabilities and capacities towards solving many problems that face them in their life’s and thinking of agroup of individuals can help to solve this situation, in the fact the problem of communication will oftenarise as a drawback to them, so computerized support tools (Artificial Intelligence and any other tools)can enable people to think and solve their problems quickly and easily and also computerizedsupporttools can improve and solve the problem of communication between individuals , processinformation to people , allow people to access different kinds of information they want at any place andmany other things .Artificial Intelligence is one of the newest and modern sciences, it had appearedafter the second World War and the name itself was coined in 1956 at the Dartmouth conference, andsince then Artificial Intelligence has expanded because of the theories and principles developed by manyresearchers, Artificial Intelligence includes a huge variety of subfields ranging from general-purposetaskssuch as learning and perception to specific tasks such as proving mathematical theories, writing poetries, and diagnosing many diseases. Before approaching the description of artificial intelligence it is veryimportant to distinguish between artificial intelligence and human intelligence, human intelligence isdefined as the ability or combination of abilities to understand and learn new knowledge and applythem in the real life situations to gain advantage over others, human intelligence means also to learnfrom the past experience and adapt yourself to the new situations. For example: if a student in theBIScollege is going to have 3 midterm exam each one after the other in one day, heshould study the wholeweek to prepare and adapt himself to the new situation that he will face in the near future.But the fieldof Artificial intelligence focuses on designing machines that can simulate human being behavior,however designing of the artificial intelligence machine or system can take time to be produced butwon’t be the Strong artificial Intelligence. In fact, some people believe that Strong artificial Intelligencesystem or machine is never possible until now due to the major differences in mechanisms between theindividual’s brain and other computer systems, in the future it is expected that artificial intelligence willhave common sense, understand knowledge and expertise like human and will be better than human.But there are some fears and uncertainty also about the future despite the good things AI can provide,as artificial Intelligence couldn’t be as we predict because this depends on scientists and researcherswho are studying and solving complex human mind and understanding different emotional feeling ofhuman, anyway artificial Intelligence has become truly a universal field. 4
  6. 6. 2. Components of AIThe machine (computer) needs to pass certain test to determine if it possess artificial intelligence or not,this test is called the TURING test: which was proposed by Alan Turing (1950), it was designed to testmachine (computer) intelligence capabilities, the test can be done by human who acts as judge betweenanother human and the machine entering the test , the test is based on answering text-questions by thejudge so judge starts asking human certain questions then asks also the machine the same questions ,ifthe answers to the questions is similar or just close enough between them that the judge can’t knowwho answered that question then the machine passes the test successfully the test does not check theability to give the correct answer “as shown in the figure”.AI has focused on the following componentsof intelligence: learning, reasoning, problem-solving, perception, and language-understanding.The computer would need to possess the following components to be intelligent justlikehuman: 1) Learning: It can be done in different ways and techniques; the simplest way is to learn from your errors or from your past experience. For example: a chess program will try all moves at random until the program success at one of its moves then the program learns and stores this move for the future so if the program face this situation again it will be an easy one to solve for the program .rote learning means to memorize things (as vocabulary)can be done on computers and also more sophisticated and new way is to learn with generalization which is concluding or extracting the answer from something that is more general so this help to improve the machine accuracy each time ,for example: a program with rote learning that learns present continuous won’t be able to double letter M in the word swim so the result is :is/are swiming if isn’t presented to the program before but the program with generalization will generalize as in the example the program will double M IN swim to become is/are +swimming. 2) Reasoning: It means to use previous acquired knowledge to answer certain questions, reasoning is based on inferences as inferences can be divided into main types deductive and inductive .deductive inference means to reason from real facts and documentations from environment to reach general conclusion so if the rules are clear and documents also are correct the final conclusion is correct .example: @all fruits are tasty , @apple is one of the fruitsthe logical conclusion is apple is tasty .inductive inference: judgment is derived from specific examples so conclusion 5
  7. 7. won’t be correct . Example: @ all men are taller than 1.85 cm, @David is man David is taller than1.85 cm wrong conclusion. Computer program must be based on deductive inference so as to produce correct conclusion for the right situation but one of the drawbacks is that computer can’t distinguish between relevant and irrelevant documents for the topics. 3) Problem solving According to many AI researchers and facts to solve any problems it requires internal understanding and representation of the problem to be able to generate possible solution for the problem.There are many algorithm for solving the problems the intelligent machine must possess ,in artificialintelligence we use many terms to understand the problem one common terms is called state: whichrepresents the solution at given step in the problem solving procedures so solution of the problem iscombination of many states the problem solving apply a given operator to the state to reach anotherstate so operator is function or method will be applied on given state and will lead to many anotherstatesthis process will continue along the problem until the desired solution is reached so this way ofsolving the problem is referred to state space approach .example: for the state space approach considertwo boxes”1 2 A 23 A 1 3Initial state desired state Now consider we define thefollowing operations for the boxes: AU: A UP, AD: A DOWN, AR: ARIGHT,AL: ALEFT. The diagram illustrates the process to move from initial state to desired state. 1 2 3 ABy AL byAU 1 A 1 2 3 2 A 3By AR BY AU BY AL BY AD A 2 1 21 2 1 3 A 1 3 A3 A 3 2 Desired state 6
  8. 8. But in this example we generate many states to reach to the solution, what about less state to reach thedesired solution so it is better AI algorithm so there are some control strategies that can be used for thisgoal: A) generate and test: it means to create many state space from the root (starting state) of theproblem and then continue this process until the goal is reached in this case you can find multiple pathsfor the goal so the path that is closer to the root is preferred so this algorithm doesn’t filter states.B) hill climbing approach: under this approach is to select the original state and measure the cost forreaching the goal from given state, so if the goal isn’t reached new point is generated from current pointand also measured with the cost, so if the goal not reached also the approach should search and selectanother starting state and do the process again. C) Heuristic search: it means to determine best stateamong available states so this will limit number of states that are within the middle area, one problemabout these techniques is this technique selection process is complex and can’t be correct. D) Meansand ends analysis: one approach is to decrease the way between current state and desired state toreach it may be simple and less costly to measure the distance between current states and desiredstates and apply specific operator to current state so the distance between current state and desiredstate is reduced. Generally problem solving methods can be divided into two main things: specific taskfor specific problems and general task for general problems as “means and ends analysis”. 4) Perception It means to feel the environment surrounding you by your organs and react to the environment so when you react to the environment you create image in your mind which is then analyzed and decomposed into separate objects with relationship between objects so object can appear different to many people depending on the angle from which it is being seen or viewed , at present artificial perception is sufficiently well capable of mean of perception with help of electronic sensors , for example: a car that drive with moderate speed on high way and also robots to clan the floor. 5) Language understanding Language is referred to system of learning and using this complex system of communication to interact with other people so to enable computer to understand language is not an easy task so programmers designed computer programs that are able to respond and interact with users such as search engine of Google you write the word and then Google try to return the best results ,one way to make machines efficient in understanding language is to watch and discover the machine mistake and to be able to fix the problems and also enabling the user to write feedback to improve the way of understanding for machines. 6) Planning It means to plan for the problems so it is one way to achieve the goals based on predefined methods and techniques .planning and reasoning differ into the structure as reasoning deal with testing the reason for the problems and the possible solution from given collections of data and knowledge also. 7) Knowledge acquisition: It means to store knowledge that is new to you as new English word you will write it to remember it .so knowledge acquisition is difficult for machines but the process that machines 7
  9. 9. follows in understanding the knowledge make it more familiar to any new knowledge ;this process consists of:1)generate knowledge from stored base,2)setting the structure for the knowledfe,3)learn the new knowledge,4)refine the knowledge. Intelligent search: It is important component for solving the problems in computer science as many problems can be solved with less time through intelligent search, one way of search is to guess the possible solutions and then refine the solutions. Another way is to use form of organisms (guesses) then select and recombine the structure to fit the problems.3. What is Artificial Intelligence?It is the science of studying, developing and restructuring machine to include intelligence so as tounderstand the surrounding environment and other people; this can be a general and abroaddefinition than other Artificial Intelligence researchers who tried to define AI, at 1985(HAUGELAND)defined AI as way that will make computer think so machines will have mind as normal humananother researcher at 1991(Kurzweil) defined AI as it is the art of creating machine that will do tasksthat require intelligence such as problem solving ,decision making. At 1998: from point of view ofNilsson defined AI as a technique that is concerned with behavior of human being, all disciplines inour life had been working toward artificial intelligence from long years, the most importantdisciplines that referred to AI is philosophy, mathematics ,psychology and computer science, whythose fields are most concerned with AI because 1) philosophy: Aristotle was the first to develop aninformal system of logical thinking for reasoning which is used to generate conclusions.2)mathematics: ALFRED TARSKI (1902-1983)introduced a theory of reference that determine howto relate objects in the world and then determine what will you do with logic and computationbeside that the theory of probability on which AI is based was invented by Italian GEROLAMO (1501-1576)that describe possible outcome from a game.3)psychology: it is concerned with studying howhuman and animal think and act and it views a brain as an information processor device which is thegoal ofAI.4)computer science: which is all about building and efficient and effective computersystems and programs to help individuals with their everyday work. Artificial intelligence is ourfuture because AI will:1)dojobs with less cost ,high efficient , with less time and effort .2)machineswon’t sleep ,won’t get ill so they will do a lot of work.3)they can help many people who lost theirterminals or disable people so they will represent the source of information , learning and teaching.4)they can help in security alerting in case of fire , crime .5)machine don’t feel so they will enforcerules and policies without mistake for example:if you are a amanager and one of your employeescame late you can simply forgive him.But besides all those advanatages there some disadantagewith machines :1)wewill depend on them and this can lead to many concequeicies as they controlour lives ,they will control us also.2)they won’t provide us with touch and quality that you expect,3)Limited sensory input compared to our brains as artificial mind is only capable of understandingsmall amount of information and also need individuals to input devices. But if you need somethingto done efficiently in less time and also with less cost or when it is too dangerous for human toperform required task you should turn to artificial intelligence. The picture below is an example of 8
  10. 10. an artificial intelligence; you ask the question then the machine answers, the answer to the question is automated.4. Characteristics of AI Systems1. Non algorithm processing: non algorithm approach contain the main logic for the application in contrast with algorithm approach which contain predefined steps for the problem to solve so non algorithm is reactive system as it will change with changing the problem, one important technique is the neural network which attempt to emulate processing pattern of the biological brain for human being and it also accepts many inputs the process the inputs and produce one single output.2. Symbolic processing: it is the attempt to create AI using programming language so as to process symbols ,symbols are like variable and they represent ideas and objects in our real world ,the goal of symbols is to construct a communication so symbolic AI process a real world entities and objects but non symbolic AI uses numbers to describe statistical patterns .symbols can be arranged in different structure such as networks, symbolic processing succeeded in in creating machine with artificial general intelligence. The major disadvantage of symbolic processing is that they can’t deal with perception, learning and simulation pattern so many AI researchers began to look into “sub symbolic theory “to solve problems that symbolic processing can’t deal with. Sub symbolic such as neural networks and fuzzy system (logic) which consists of variety of techniques and methods for representing knowledge and information that is imprecise and uncertain, fuzzy logic created rules to deal with uncertainty so fuzzy system are closer to the way people think than traditional. 9
  11. 11. 3. High performance: AI systems should be capable of performing tasks with high speed and with efficiency also as they were designed with intelligence, they are expected that they can reach level of performance equal to or exceed human experts.5. Application of AITypical artificial intelligence applications are: A. Game playing: as many youth interesting in paying games and they want to every competition they want to enter against anyone, now AI machines can offer youth what they want as AI machines can try all possibilities to achieve winning strategy against competitors for example: in chess game you can just easily beat the world champion player by just using AI machines as a AI machines can look for 200 million positions per second. B. Speech recognition: it is the translation of spoken language by individuals into text understandable to do specific things .for example: voice dialing (I want to call work), data entry by voice (storing and entering different products by human being on the computer), it is possible to construct computer using speech recognition so users can just talk to computer and don’t use keyboard and mouse. C. Understanding natural language: problems of understanding language depends on syntactic and semantic meaning of the phrases so syntactic required to correct grammar mistakes and analyze sentences and semantics is performed after the syntactic to determine meaning of phrases and the linkage between words to be understandable . So a robot should understand natural language as it is classified as intelligent machine. D. Expert systems “will be discussed in more details”: it is the core AI application ,it is a decision making or problem solving software package that attempt to reach level of performance that emulate human expert problem expert system consists of :1)knowledge base: it is collection of huge information extracted from human experts ,2)inference engine: it is the engine that is responsible for taking orders and information from the user .so it is the processing elements among other components and it makes the use of knowledge base to generate conclusion for many situatioins,3)working memory: contain data received from the users so knowledge base can create ,update and delete in working memory,4)other components includes: A)user interface: user communicates and commands expert system through this system. B) Explanation mechanism: it is the way for reaching general conclusion and it is very important in reasoning process. 10
  12. 12. E. Robotics and navigation: robots are machine designed to perform complex tasks and serve human being in their daily life, robots at the past was capable to actions that has been before and couldn’t do new actions based on their own but now AI researchers and scientists developed robotics to include artificial intelligence so robotics can now mimic human actions, new term is introduced and called autonomous robotics which is referred to capability of robots to adapt themselves with surrounding environment and learn new things also.  This diagram shows disciplines of AI along with AI components and applications areas of AI; this diagram describes what artificial intelligence is all about.AI has been used along many fields such as: 1) Finance: banks use intelligent software to analyze financial data and financial market fluctuations and to predict stock prices in stock markets. 2) Hospital and medicine: hospitals can now use AI to arrange beds, to check for empty room if patient is coming new to the hospital and help doctors to periodically check their patient; neural network can help in supporting decisions for the whole clinic. 11
  13. 13. Expert system INTRODUCTIONExpert systems are computer based systems that uses knowledge and reasoning techniques to solve theproblem that require human experience knowledge from experts and other sources such as text booksand journal articles entered into a system in a coded form which is then used by the system reasoningpresses to offer advice on requestExpert systems belong to border discipline of artificial intelligence which has been defined by barr andFEIGENBAUM in 1981 as the part of computer science that is concerned with designing intelligentcomputer systems meaning that designing system that can have characteristics such as learninglanguage and understanding problemsArtificial intelligence as a separate discipline started in 1950 when it was recognized that the computerswere just a giant calculators dealing only with numbers so after inventing the artificial intelligence itspread rapidly in making robots machine industry and most of the practical fieldsHISTORY OF EXPERT SYSTEMExpert systems were introduced by researchers in the Stanford Heuristic Programming Project, includingthe "father of expert systems" with the Dendral and Mycin systems. Principal contributors to thetechnology were Bruce Buchanan, Edward Shortliffe, Randall Davis, William vanmelle, Carli Scott andothers at Stanford. Expert systems were among the first truly successful forms of AI softwareResearch is also very active in France, where researchers focused on the automation of reasoning andlogic engines. The French Prolog computer language, designed in 1972, marks a real advance over expertsystems like Dendral or Mycin: it is a shell,[16] that is to say, a software structure ready to receive anyexpert system and to run it. Prolog has an integrated inference engine using First-Order logic, with rulesand facts. Prolog is a tool for mass production of expert systems and was the first operationaldeclarative language later becoming the bestselling AI language in the world However Prolog is notparticularly user friendly and incorporates Horn Logic, which is an order of logic away from human logic.In 1981 the first IBM PC was introduced, with MS-DOS operating system. Its low price started to multiplyusers and opened a new market for computing and expert systems. In the 80s the image of AI was verygood and people believed it would succeed within a short timeThe development of expert systems was aided by the development of the symbolic processinglanguages Lisp and Prolog. To avoid re-inventing the wheel, expert system shells were created that hadmore specialized features for building large systems 12
  14. 14. EXPERT SYSTEM DEVELOPMENTKnowledge engineers expect to work with systems that cannot be well defined in advance. Theinteraction phases with the user are crucial to the development of the expert system. Theprocess tends to be circular rather than linear (Fig. A2). The original rules developed may laterbe rewritten entirely or dropped, altogether as the experts and knowledge engineers graduallyrefine their understanding of the knowledge that must be included in the knowledge base.Interaction with the user in the early stages is crucial. Typically, steps in expert systemdevelopment includes:Front-end analysis: Problem identification, cost and effectiveness requirements, stakeholderbuy-in.Task analysis: Identify task(s), behavioral sequence and required knowledge.Prototype development: Identify case studies, develop small scale system to prove concept andprovide practice.System development: Rearrange overall structure as required, add knowledge.Field testing: Test system with actual users, revise as necessary.Implementation: Port system to hardware to be used in the field; train users to use the system.Maintenance: Establish means to update the system, update as required.The development is the result of a collaborative effort from among knowledge engineers,domain experts and end-users. The knowledge engineer acts as a bridge between the domainexpert and the knowledge encapsulation environment. The tasks are:- Acquire knowledge from domain experts; formalize terms, eliminate vagueness andinconsistencies- Model and organize information received from domain experts- Integrate the facts, rules, objects and relationship information into the expert system sourcecode.ert systemsHOW DOES THE EXPERT SYSTEM WORKAn expert system is made up of three parts:1. A user interface - This is the system that allows a non-expert user to query (question) theexpert system, and to receive advice. The user-interface is designed to be a simple to use aspossible.2. A knowledge base - This is a collection of facts and rules. The knowledge base is createdfrom information provided by human experts 13
  15. 15. 3. An inference engine - these acts rather like a search engine, examining the knowledge base forinformation that matches the users queryKnowledge representation Knowledge representation (KR) is an area of artificial intelligence that aimed at representing knowledge in symbols to facilitate the process; KR research involves analysis of how to reason and effectively and to use symbols to represent a set of facts within a knowledge domain. A good knowledge representation covers six basic characteristics: 1. Coverage: which means the KR covers many of the information. Without a wide coverage, the KR cannot resolve any problems that face it. 2. Understandable by humans. KR is viewed as a natural language, so it is simple to be understood and will contain simple logic 3. Consistency: KR can eliminate redundant knowledge so as not to be confused. 4. Efficient: KR will represent knowledge with more speed and at high level of correctness 5. Easiness: for altering, deleting and updating data. 6. Supports the intelligent activity which uses the knowledge baseProblems with KROne problem in knowledge representation is how to store and retrieve knowledge easily ininformation system.THE RULE BASE OR KNOWLEDGE BASE SYTEMIn expert system technology, the knowledge base is expressed with natural language rules IF Forexamples:"IF it is living THEN it is mortal""IF his age = known THEN his year of birth = current year - his age in years""IF the identity of the germ is not known with certainty AND the germ is gram-positive AND themorphology of the organism is "rod" AND the germ is aerobic THEN there is a strong probability (0.8)that the germ is of type enterobacteriacaeThis formulation has the advantage of speaking in everyday language which is very rare in computerscience (a classic program is coded). Rules express the knowledge to be exploited by the expert system.There exist other formulations of rules, which are not in everyday language, understandable only tocomputer scientists. Each rule style is adapted to an engine style. 14
  16. 16. BENEFITS OF EXPERT SYSTEMSPermanence: Expert systems do not forget.Reproducibility: Copies of an expert system can be made.Power: For applications where there is a maze of rules exhibited, it can be unravelled by the expertsystem.Efficiency: Expert systems can increase throughput and reduce personnel costs.- Expert systems are inexpensive to operate.- Development costs can be amortized over many years.- Expert systems can eliminate routine costs and reduce major maintenance costs.Consistency: With expert systems, similar events are handled the same way. Expert systems will makecomparable recommendations for like situations and are not affected by recent or primary effects.Documentation: Expert systems provide permanent documentation of the decision process.Completeness: An expert system can review all the transactions or possibilities.Timeliness: Fraud and/or errors can be prevented. Information is available sooner for decision makingand action. The expert system works 24 hours a day, all year long.Scope: The expert system can encompass the cumulative expertise of many human experts.Business success: Owners reduce the inherent risks of conducting their business due to:- Consistency of decision making.- Documentation (ISO requirements)- Acquired expertisePositive impacts:- Productivity gains and cost savings.- Critical new tool for managers and a proactive answer to expertise attrition.- Decisions and solutions are more consistent and less subject to biases or sensitivity to the environment- Employment: shift to-wards more satisfying work. ETEXPERT SYSTEM STRUCTUREThe structure and operation of an expert system are. Experts use their knowledge about a given domain coupled with specific information about thecurrent problem to arrive at a solution. For Example, a physician wouldpossess knowledge about varietyof possible diseases and, coupled with specific Information about a given patient, would be able todiagnose the patients problem.Expert systems solve problems using a process which Is very similar to the methods used by the humanexpert 15
  17. 17. ADVANTAGES1-CONVERSATIONALExpert systems offer many advantages for users when compared to traditional programs because theyoperate like a human brain2-QUICK AVAILABILITY AND OPPORTUNITY TO PROGRAM ITSELFAs the rule base is in everyday language (the engine is untouchable), expert system can be written muchfaster than a conventional program, by users or experts, bypassing professional developers and avoidingthe need to explain the subject.3-ABILITY TO EXPLOIT A CONSIDERABLE AMOUNT OF KNOWLEDGEThe expert system uses a rule base, unlike conventional programs, which means that the volume ofknowledge to program is not a major concern. Whether the rule base has 10 rules or 10 000, theengine operation is the same.4-RELIABILITYThe reliability of an expert system is the same as the reliability of a database, i.e. good, higher thanthat of a classical program. It also depends on the size of knowledge base.5-SCALABILITYEvolving an expert system is to add, modify or delete rules. Since the rules are written in6-PEDAGOGY 16
  18. 18. The engines that are run by a true logic are able to explain to the user in plain language why theyask a question and how they arrived at each deduction. In doing so, they show knowledge of theexpert contained in the expert system. So, user can learn this knowledge in its context. Moreover,they can communicate their deductions step by step. So, the user has information about theirproblem even before the final answer of the expert system.DISADVATAGESEvery expert system has a major flaw, which explains its low success despite the principles that it isbased upon having existed for 70 years: knowledge collection and its interpretation into rules,Every expert system has a major flaw, which explains its low success despite the principles that it isbased upon having existed for 70 years: knowledge collection and its interpretation into rules, orknowledge engineering. Most developers have no automated method to perform this task; insteadthey work manually, increasing the likelihood of errors. Expert knowledge is generally not wellunderstood; for example, rules may not exist, be contradictory, or be poorly written and unusable.Worse still, most expert systems use a computational engine incapable of reasoning. As a result, anexpert system will often work poorly, and the project will be abandoned. Correct developmentmethodology can mitigate these problems. There exists software capable of interviewing a trueexpert on a subject and automatically writing the rule base, or knowledge base, from the answers.The expert system can then be simultaneously run before the true experts eyes, performing aconsistency of rules check. Experts and users can check the quality of the software before it is finishedMany expert systems are also penalized by the logic used. Most formal systems of logic operateon variable facts, i.e. facts the value of which changes several times during one reasoning. This isconsidered a property belonging to more powerful logic.Case-based reasoning:-It is the process of solving the problems using the solution of identical old problem or from pastexperience. It isn’t used only for computers but it is powerful for everyday life.Ex) An auto mechanic who fixes an engine by that exhibited similar symptoms is using case-basedreasoning.Case-based reasoning:-Process:- 1. Retrieve: Given the problem. Use the memory to select similar case to solve the problem. A case consists of a problem, its solution, and, typically, the way about how the solution was derived. 2. Reuse: this step determines how the solution is derived from previous cases to the target problem. This may involve adapting the solution as needed to fit the new situation. 3. Revise: as you get the solution to the target situation, test the new solution in the real situations, then revise. 4. Retain: After the solution is obtained for the problem and adapted, store the results of the problem in the memory for further use. 17
  19. 19. Criticism about case base reasoning: case base reasoning is based on generalization rules and generalization rules can’t be correct so case base can provide many uncorrected information so there is a recent work that developers will assign case base reasoning with statistical framework so it will be based on probability that will tell you the information with percentage of confidence and reliabilityInference engine:-In the field of computer science, and artificial intelligence, an inference engine: is computer softwarethat tries to find and extract answers from data and information that is stored in the knowledge baseusing many methods of artificial intelligence, In order to produce a reasoning, it should be based onlogic. There are several kinds of logic: propositional logic, predicates of order 1 or more, epistemic logic,modal logic, temporal logic, fuzzy logic, etc. Except for propositional logic, all are complex and can onlybe understood by mathematicians, logicians or computer scientists. Propositional logic is the basichuman logic that is expressed in syllogisms. The expert system that uses that logic is also called a zeroth-order expert system. With logic, the engine is able to generate new information from the knowledgecontained in the rule base and data to be processed.. A strong interest in using logic is that this kind ofsoftware is able to give the user clear explanation of what it is doing and what it has deduced Better yet,thanks to logic, the most sophisticated expert systems are able to detect contradictions in userinformation or in the knowledge and can explain them clearly, revealing at the same time the expertsknowledge and way of thinking. It is the attempt to emulate some process of the brain system and manyexpert systems use this brain justify the information they need to achieve certain goal from the 18
  20. 20. knowledge, inference engines are also capable of performing logical processing, and can do manyprobability calculations to reach conclusions that the knowledge database can’t doThe program uses inference engine can be seen as a mechanism for selective active where theprocessing operations are directed by the recent value of data, expert system have two general methodsof processing the stored data (forward chaining, backward chaining), the rules of the expert system is toanalyze data by the means of the inference engine, and the results are feed back into the systems datastorage as new data. The two methods are: 1. Forward chaining: is a method of reasoning, it startswith data available on hand and uses inferences to increase amount of data related to the objective thatwill be achieved so forward chaining depends on searching all possibilities until it concludes the finalresult so after the process of finding data and the goal is reached, it adds new information to its owndata.For example: suppose our objective is to reach whether “a cat named kitty eats mouse or not”. Supposethe following rule bases: 1) if X is a cat then X is black, 2) if X is black then X eats mouse. So forwardchaining will be: 1) kitty is a cat,2) kitty is domestic,3) kitty eats mouse. Forward reasoning will be: 1)kitty is a cat and kitty is domestic,2)kitty is a cat and is domestic. so 3)kitty eats mouse. In this examplecomputer process data and tries to reach the objective so this method of searching is called data-driven.2. Backward chaining: it is the opposite to forward chaining as it works backward toward the targetedobjective so backward chaining starts by listing the objectives and then works backward searching forthe data that deliver to the objective.In the previous of forward reasoning example “a cat named kitty eats mouse or not “, backwardreasoning will work in the opposite direction: 1)? Does kitty eats mouse, 2)? Is black and eats mouse, 3)kitty is black and kitty eats mouse. So the computer derived to the objective by first questioning aboutthe goal itself. This method is called goal-driven, so backward chaining is often used by expert system.Comparison between advantages and disadvantages of the techniquesAdvantages of forward chaining disadvantages of forward chaining 1) Can provide large amount of Take much time as the system will try information from small amount of and ask all questions that could deliver data. to the correct answer. 2) It fits many management tasks such as: planning, controlling and monitoring. 19
  21. 21. Advantages of backward chaining disadvantages of backward chaining 1) It focuses in the goals so less time is It will use specific way of reasoning consumed and specific questions is even if this way isn’t required as system asked and will lead to the correct will switch to new one. answer. 2) It can be used in debugging ,diagnostics and many others  Those two techniques are used by expert system, many expert systems use both forward chaining and backward chaining so for a given situation expert system can use one of the techniques that will achieve the goal directly and leave the other technique.  Inference engine relies on: 1) interpreter: executes the next step by applying specific rules on it,2)scheduler: it maintains controls over steps in interpreter by measuring the effects of applying information rules.3)consistency enforcer: the foal of this components to assure consistency and provide consistency solution.The recognize-act cycle:-The inference engine can represent many states with cycle processing of three action states: matchrules, select rules, and execute rules. 1) Match rule: the inference engine finds all of the rules that by thecurrent contents of the data store.2) select rules: applies some selection strategy to determine whichrules will actually be executed. The selection strategy can be coded into the engine .3) execute rules:executes or fires the selected rules.Data-driven computation versus procedural control:-The inference engine control is performing testing of the data store states. This is referred to qualifydata-driven in contrast to the more traditional procedural control: in which information about theproblem is combined with instructions about the control, the inference engine model allows a morecomplete separation of the knowledge from the control (the inference engine). 20
  22. 22. INTELLIGENT TECHNIQUES This techniques is considered as apart of artificial intelligence which are : Intelligent agentsIntelligent agents: automatic entities which direct their activities to accomplish certain goals, so we candescribe it as RATIONAL. These entities may be very simple or extremely complex. Intelligent agents aresometimes described as abstract units. Intelligent agents in artificial intelligence can be related toeconomics.IA: has been defined in many ways:-For Nikola KASABOV: system must show the following aspects:- 21
  23. 23. 1- Accommodate problem solving techniques (adopt a way that similar to the expert system which always learn and can act by it self 2- Can act online and in the same time(real time) 3- Ability of analyze itself in terms of acting error& success 4- Learn new techniques and demands from the working environment 5- Can store and retrieveClasses of intelligent agents:-Divided into 5 classes based on their degree of intelligence and capability:- 1- Simple reflex agents: act only on the basis of the current principle or in other words the simple reflex is based On: if condition then action. This agent only operates in an environment that is only observable. So this agent always uses the infinite loop but in the case that the agent could randomize its actions it may be possible to escape this infinite loop. 2- Model-based reflex agents: can deal with partially observable environment. it should adopt an internal model approach 3- Goal-based agents: a model that reflect the desired outcomes. This allows the agent to pick a solution among many multiples so it can select the option that will achieve the set goal 4- Utility-based agents: can define between goal states and non-goal state .it defines a measure to tell the difference which is can be generated through the use of utility function which maps the difference in the outcomes of every state 5- Rational utility-based system: pick up the action associated with the highest outcome 6- Learning agents: allows the agent to operate on unknown environment where there are no explicit data or clear information. In that agent model we must differentiate between two definitions firstly :-  The learning element which is responsible for making innovations and improvements.  The performance element: responsible for choosing the external actions. 22
  24. 24. Structure of the agentIs a simple agents which is defined as [Agent function]. It also planned to arrange definition to possibleaction that can depend and perform on it. The function that affect at the end of these action is:Other classes for the intelligent agents:- 1. Decision agents : supports the decision making process 2. Input agents: This agent is like a process that predicts and makes sense about input sensors. 3. Process agents: solving problems e.g. speech recognition 4. World agents: combine different classes to provide the automatic behavior (random)behavior 5. Physical agents : an entity acts through sensors and actuators 6. Temporal agents: use stored information to offer instructions. Intelligent agents are used in automated online assistants where they receive customers inquiries and trying to solve it automatically EX: the dialogue system , an avatar , and an expert systemNeural networksThe term refers to a biological circuit, the modern usage o f the term often refers to artificial neuralsystem .which is composed of artificial nodes, these networks may be used for predicative modelingA biological neural network consists of a group of chemically connected historically; digital computersevolved from the von Neumann model, and operate via the execution of explicit instructions via accessto memory by a number of processors. On the other hand, the origins of neural networks are based onefforts to model information processing in biological systems. Unlike the von Neumann model, neuralnetwork computing does not separate memory and processing. Criticism or disadvantages :-  Requires a large and complex training for real-world operation. But that is natural as every learning machine needs sufficient training in in order to implement it in the real world.Hybrid SystemThe objective of computerized-based information system is to assist management for solving problemsinside organization. The managerial decision making process combined with MSS technologies in solvingproblems. From the cognitive science prospective every natural intelligent system is hybrid because itperforms mental operations. So hybrid system may combines different techniques as: fuzzy logic ,genetic fuzzy system. The integration of different learning and adaptation techniques, to overcomeindividual limitations and achieve synergetic effects through hybridization or fusion of these techniques, 23
  25. 25. has in recent years contributed to an emergence of large number of new superior class of intelligenceknown as Hybrid Intelligence.The problems in MSS it can be solved by employee’s different tools techniques:1) These tools must be independence to solve problems and making decision process.2) Using several integrated tools._The goal of hybrid system is reaching a good and successful decision to solving problems._Hybrid system provides information techniques and performs many tasks and supports each of them._They working together to reach many answers and producing smart answers to solve problems.  Fuzzy logic: this technique consists of variety of concepts and techniques for representing many uncertain events and information so fuzzy logic creates and enforces many rules that deals with subjective values and uncertain data so fuzzy logic is so closer to the way people think.  Genetic algorithm: refers to the adaptive computation so it consists of variety of problem solving methods that promote evolution of solution to specific problems using the model of living organism adapting to their environment so it can be used to maximize profit in advertising field. 24
  26. 26. REFRENCES For artificial intelligence 1. 2. Decision Support Systems and Intelligent Systems (7th Edition) PDF.pdf 3. Russell S., Norvig P. Artificial intelligence- a modern approach (2ed,PH,2003)(T)(1112s).pdf 4. human-intelligence/ 5. Wikipedia 6. AI.html 7. 8. The_Pros_and_Cons_of_AI 9. 10. intelligence.php For INFERENCE ENGINE AND CASE BASE REASONING1- http://en.wikipedia.org3- For expert system1. Wikipedia2. Preview to expert system by ALN3. Priprinciples. of expert system For genetic algorithm and intelligent agent and neural network, hybrid system fuzzy logic: 1. Wikipedia Dss book 25