Artificial Intelligence - A Brief Introduction And Application Examples
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  • 1. Artificial Intelligence: A Brief Introduction and application examples Ling & Lam 2009
  • 2. Universiti Tunku Abdul Rahman Table of Contents Lists of Figures and Tables........................................................................................................ 2 Figures ................................................................................................................................... 2 Tables .................................................................................................................................... 2 Chapter 1 Introduction............................................................................................................... 3 Chapter 2 Applications of AI .................................................................................................... 4 2.1 Games .............................................................................................................................. 4 2.2 Expert System.................................................................................................................. 5 2.3 Intelligent Agent .............................................................................................................. 6 2.4 Simulations ...................................................................................................................... 7 2.5 Computer Vision ............................................................................................................. 8 2.6 Natural Language Processing ........................................................................................ 10 2.7 Machine Learning.......................................................................................................... 12 2.8 Interfaces ....................................................................................................................... 12 2.9 Robotics ......................................................................................................................... 13 2.10 Theorem Proving ......................................................................................................... 14 Chapter 3 Expert System ......................................................................................................... 16 3.1 Introduction ................................................................................................................... 16 3.2 Basic Concepts of Expert Systems ................................................................................ 17 3.4 How Expert Systems Work ........................................................................................... 18 3.5 Expert Systems in Medical Field ................................................................................... 21 3.6 Pros and Cons of Expert Systems .................................................................................. 22 3.7 Conclusion ..................................................................................................................... 22 Chapter 4 Conclusion .............................................................................................................. 24 Reference ................................................................................................................................. 24 1
  • 3. Universiti Tunku Abdul Rahman Lists of Figures and Tables Figures Figure 1 Deep Blue…………………………………………………….………………………5 Figure 2 & Figure 3 Flight Simulation and Auto Racing Simulation………………………….8 Figure 4 Video surveillance system software………………………………………………...10 Figure 5 A map of ALICE's "brain" plots all the words she knows….………………………11 Figure 6 & Figure 7 Android Aiko (Left) and Lisa (Right)…………………………………..14 Figure 8 Process of transferring expertise……………………………………………………18 Figure 9 Main components of expert systems and their interrelationship……………………20 Tables Table 1 Types of expert systems……………………………………………………………...16 Table 2 Lists of well-known expert systems in medical field………………………………..21 Table 3 Application example in different categories of medical expert systems…………….21 2
  • 4. Universiti Tunku Abdul Rahman Chapter 1 Introduction Artificial Intelligence (AI), a term that coined by John McCarthy in his 1955 proposal for the 1956 Dartmouth Conference, is the branch of computer science concerned with making computers behave like humans, or defined by John McCarthy himself as “the science and engineering of making intelligent machines, especially intelligent computer programs.” After Second World War, a number of people started to work on intelligent machines independently, where it is believed that that was the time AI research started. The English mathematician Alan Turing may have been the first where he gave a lecture on it in 1947. He also may have been the first to decide that AI was best researched by programming computers rather than by building machines. By the late 1950s, there were many researchers on AI, and most of them were basing their work on programming computers. Although it has been around 60 years in AI research where most of the researcher aimed to simulate full human behavior in various applications, however, up until today, there is still no computers exhibit full artificial intelligence. Nevertheless, research in AI never stopped where there are still a lot of different research projects focusing on AI improvement. One of the noticeable projects is the Artificial Intelligence System (AIS), which is a large scale distributed computing project by Intelligence Realm, Inc. that involving over 10,000 computers with the initial goal to recreate the largest brain simulation to date. The AIS project has successfully simulated over 700 billion neurons as of April 28, 2009. Although the failure for appearance of AI that fully mimics human behavior, there are numerous of fields, such as natural resources management, medicine, military, petroleum industry, just to mention a few, has been make full use of AI to achieve different goals in a smarter way nowadays. Some of the AI applications have been proven to give great practical benefits, and despite of the widely use of AI techniques in software and hardware, the existence of AI in all those products mostly go unnoticed by many people, which also known as the “AI Effect”. Thus, it is not hard to observe that AI is so important that it has been a part of life in every industrialized nation. In the next section, there will be some examples on the application of AI in different fields or industries. 3
  • 5. Universiti Tunku Abdul Rahman Chapter 2 Applications of AI 2.1 Games One of the most studied and most interesting areas of AI is the application of AI in games and simulations. AI has been extensively used many type of games, including board games like chess, checker; computer games like strategy games and massively multiplayer online role-playing game (MMORPG). With the integration of AI, a game will become livelier and more fun to the user as the AI used is simulating the real environment or human behavior in the game. AI applied in games somewhat complex if compared to those AI applied in problem solving system which can perform precise and accurate decision, such as expert system. AI for game is about the imitation of human behaviour where it has to be  Creative and Smart (to a certain extent),  Non-repeating behaviour,  Unpredictable but rational decisions,  Emotional influences or personality,  Body language to communicate emotions,  Being integrated in the environment, so that the user are able to experience the somewhat „human-like‟ response on any action being taken in the game. Early research of AI application in game playing (state space search) was done using common board games such as checkers, chess, and the 15-puzzle. These games are chosen to be the subject not only because they have limited and well defined rules, but they also have huge state spaces due to the complexity of the game which means that a perfect move is almost impossible in limited time. One of the most successful examples of the game playing AI in the history is the Deep Blue, a chess-playing computer that derived its playing strength mainly out of brute force computing power, which is developed by IBM. On May 11, 1997, the machine won a six-game match by two wins to one with three draws against world champion Garry Kasparov. 4
  • 6. Universiti Tunku Abdul Rahman Figure 1 Deep Blue 2.2 Expert System AI programs that achieve expert-level competence in solving problems in task areas by bringing to bear a body of knowledge about specific tasks are called expert systems. Often, the term expert systems is reserved for programs whose knowledge base contains the knowledge used by human experts, in contrast to knowledge gathered from textbooks or non-experts. The primary goal of expert systems research is to make expertise available to decision makers and technicians who need answers quickly. There is always not enough expertise to go around solving problem at the right place and the right time. Expert systems can assist supervisors and managers with situation assessment and long-range planning. Many small systems now exist that bring a narrow slice of in-depth knowledge to a specific problem, and these provide evidence that the broader goal is achievable. Expert systems have been widely used in a number of industries; one of the examples that utilize it extensively is the aviation industry. The scheduling flights based on economics, environmental, regulatory requirements and airway traffic parameters are extremely complex. Any mistakes in any one of the steps involved in a flight is extremely costly. Typically, expert systems that can be found in aviation industry include: 5
  • 7. Universiti Tunku Abdul Rahman  The Aviation Expert System This is an expert system that used to clarify psychological assessment issues in the field of aviation.  General Aviation Pilot Advisor and Training System (GAPATS) GAPATS is a computerized airborne expert system that uses fuzzy logic to infer the flight mode of an aircraft from: o sensed flight parameters o an embedded knowledge base, and o pilot inputs which the data will then used to assess the pilot‟s flying performance and issue recommendations for pilot actions.  Aircraft Maintenance Expert Systems (AMES) AMES has been used since the early 1990's. Manual procedures around aircraft maintenance are very strenuous and time consuming. Diagnosis of aircraft malfunctions is an ideal candidate for an expert system to assist in the diagnosis of aircraft problems.  Anti-G Fighter Pilot System The high maneuverability of modern jet fighters often subjects the pilots to high Gz acceleration. One of the adverse effects of Gz acceleration is loss of consciousness. The Anti-G Fighter Pilot System presents an alternative to the current protection pressure mask and pressurized G-suit. The system used expert knowledge and pilots' anthropometric and physiologic data to generate control schedules of the G-suit and mask pressures of jet fighter pilots. 2.3 Intelligent Agent In AI, an intelligent agent (IA) is an autonomous entity which observes and acts upon an environment and directs its activity towards achieving goals. Intelligent agents may also learn or use knowledge to achieve their goals. 6
  • 8. Universiti Tunku Abdul Rahman They may be very simple or very complex; for example, a reflex machine such as a thermostat is an intelligent agent. Based on the intelligent agent‟s degree of perceived intelligence and capability, Russell & Norvig categorize agents into five classes: i. simple reflex agents ii. model-based reflex agents iii. goal-based agents iv. utility-based agents v. learning agents. However, intelligent agents nowadays are normally gathered in a hierarchical structure containing many “sub-agents” in order to perform more advance tasks. Intelligent sub-agents process and perform lower level functions. Taken together, the intelligent agent and sub-agents create a complete system that able to accomplish difficult tasks or goals with behaviors and responses that display a form of intelligence. In the world of e-commerce, intelligent agents known as shopping bots are used by consumers to search for product and pricing information on the Web. Each shopping bot operates differently, depending on the business model used by its operator. Famous internet search engine like Google, Yahoo!, and Ask.com are also utilizing this technology to perform information searching for the internet user. 2.4 Simulations Besides the AI used in game playing with the purpose of having fun, simulations nowadays has heavily written code for AI to simulate the most realistic feedback and response for any action by the user; to let the user feels that he is placed in that particular environment or situation. This is very important for the industries that utilizing simulations, such as the aviation industry and auto racing because any slight mistake in a flight or in a race is very costly, even death. Thus, simulation is used to train the user familiar with some events that rarely happen but able to respond swiftly if that particular 7
  • 9. Universiti Tunku Abdul Rahman event really happens. In a simulator, every input of the user will get responded by the most accurate feedback to ensure that the user is able to experience it before he/she go for the real thing, especially in auto racing industry. The application of artificial intelligence in simulators is proving to be very useful for the aviation industry. Airplane simulators are using artificial intelligence in order to process the data taken from simulated flights. Other than simulated flying, there is also simulated aircraft warfare. The computers are able to come up with the best success scenarios in these situations. The computers can also create strategies based on the placement, size, speed, and strength of the forces and counter forces. Pilots may be given assistance in the air during combat by computers. The artificial intelligent programs can sort the information and provide the pilot with the best possible maneuvers, not to mention getting rid of certain maneuvers that would be impossible for a sentient being to perform. Multiple aircraft are needed to get good approximations for some calculations so computer simulated pilots are used to gather data. These computer simulated pilots are also used to train future air traffic controllers. Figure 2 & Figure 3 Flight Simulation and Auto Racing Simulation 2.5 Computer Vision Computer vision is the branch of artificial intelligence that focuses on providing computers with the functions typical of human vision. Computer 8
  • 10. Universiti Tunku Abdul Rahman vision has been applied in several fields such as industrial automation, robotics, biomedicine, and satellite observation of Earth. In the field of industrial automation alone, its applications include guidance for robots to correctly pick up and place manufactured parts, nondestructive quality and integrity inspection, and on-line measurements. When the famous dedicated computer-vision system, the Massively Parallel Processor (MPP) designed at the Goddard Space Flight Center in 1983, it does not received good response due to the complexity and very high price. However, with the advancement in manufacturing technology, the availability of affordable hardware and software has opened the way for new, pervasive applications of computer vision. Computer-vision systems have one factor in common. They tend to be human-centered; that is, either humans are the targets of the vision system or they wander about wearing small cameras, or sometimes both. Nowadays, computer-vision systems have been used in quite a number of applications, including: o human-computer interfaces (HCIs), the links between computers and their users o augmented perception, tools that increase normal perception capabilities of humans o automatic media interpretation, which provides an understanding of the content of modern digital media, such as videos and movies, without the need for human intervention or annotation o video surveillance and biometrics. Although the success of the usage of computer vision in our daily life, to date, computer vision systems still unable to emulate the full capabilities of the human visual system. The human eye-brain combination still proved to be the best which is able to categorize previously unseen objects with ease, using background knowledge and context. Nevertheless, computer vision has helped to solve a lot of problems and difficulties in human‟s life; hence it is 9
  • 11. Universiti Tunku Abdul Rahman foreseeable that the usage of computer vision will continue to grow to improve humans‟ life. Figure 4 Video surveillance system software 2.6 Natural Language Processing Natural Language Processing (NLP) is both a modern computational technology and a method of investigating and evaluating claims about human (natural) language itself. NLP uses computers to process written and spoken language for some practical, useful, purpose: to translate languages, to get information from the database to answer an enquiry, to carry on conversations with machines, so as to get advice about, say, pensions and so on. And these are only examples of major types of NLP. There is also a huge range of lesser but interesting applications, e.g. getting a computer to decide if one newspaper story has been rewritten from another or not. Hence, NLP is not simply applications but the core technical methods and theories that the major tasks above divide up into, such as Machine Learning techniques, which is automating the construction and adaptation of machine dictionaries, modeling human agents' beliefs and desires etc. Artificial Intelligence is an essential component of NLP if the computers have to engage in realistic conversations with human. 10
  • 12. Universiti Tunku Abdul Rahman One of the modern AI research in NLP is the research in chatterbot. A chatterbot (or chatbot) is a type of conversational agent, a computer program designed to simulate an intelligent conversation with one or more human users via auditory or textual methods. Also known as Artificial Conversational Entity (ACE) and, though many appear to be intelligently interpreting the human input prior to providing a response, most of the chatterbots simply look for keywords within the input and reply with the most matching keywords or the most similar wording pattern from a local database. The classic and early famous chatterbots are ELIZA (1966) and PARRY (1972). More recent programs are Racter, Verbots, A.L.I.C.E., SmarterChild, and ELLA. With the growing number of research in chatterbots, the initial purpose of creating chatterbots has been expanded to many other usages, for example story „writing‟ by Racter. Some of the organizations are already beginning to implement a so-called Automated Conversational Systems which is a system that evolved from the original designs of the first widely used chatbots. In the UK, large commercial entities such as Lloyds TSB, Royal Bank of Scotland, Renault, Citroë and One n Railway are already utilizing Virtual Assistants to reduce expenditures on Call Centres and provide a first point of contact that can inform the user exactly of points of interest, provide support, capture data from the user and promote products for sale. Figure 5 A map of ALICE's "brain" plots all the words she knows. 11
  • 13. Universiti Tunku Abdul Rahman 2.7 Machine Learning Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to learn based on data, such as from sensor data or databases. A major focus of machine learning research is to automatically learn from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. Expert systems and data mining programs are the most common applications for improving algorithms through the use of machine learning. Among the most common approaches are the use of artificial neural networks (weighted decision paths) and genetic algorithms (algorithms “bred” and culled to produce successively fitter programs). Due to the nature of machine learning, its‟ capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15–25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve basic understanding of cancer development and progression. A trend can be seen that machine learning has been used quite often in a lot of different systems, be it a diagnostic system or education system, due to the long term benefit. The success of machine learning programs suggests the existence of a set of general learning principles that will allow the construction of programs with the ability to learn in realistic domains. 2.8 Interfaces Even the most sophisticated and powerful system will be next to useless without an effective user interface. A good and user-friendly interfaces that controls a complex machine is must-equipped software to allow the interaction between the user and the machine. However, with the rapid advancement in 12
  • 14. Universiti Tunku Abdul Rahman technology, the demand in interfaces become higher and higher. Most of the users already started to ask for a smarter interface to learn and remember their preferences, instead of having the user input the same preference everytime and thus, interface agents are started to get developed due to the demand. Interface agents are computer programs that employ Artificial Intelligence methods to provide active assistance to a user of a particular computer application. The metaphor used is that of a personal assistant who is collaborating with the user in the same work environment. The assistant becomes gradually more effective as it learns the user's interests, habits and preferences. One of the examples of commercialized interface agent has been used in Microsoft Windows Vista called SuperFetch, a feature that predicts which applications are used when, then pre-loads them so that they're instantly available. „Microsoft Research contributed to the SuperFetch effort, a feature within Vista that predicts which applications are used when, then pre-loads them so that they're instantly available.‟ „As part of a long term set of projects, we want to teach the computer to learn from users to make the machine more proactive,‟ says Eric Horvitz, a principal researcher with Microsoft's R&D as well as the president- elect of the American Association for Artificial Intelligence. „We want to use the system's idle time to make things punchier.‟ Therefore, it is not surprising that the interface agent become part of the important components, no matter in the hardware or in the software, due to the high demand of human. 2.9 Robotics Robots have become common in many industries. They are often given jobs that are considered dangerous to humans. Robots have proven effective in jobs that are very repetitive which may lead to mistakes or accidents due to a lapse in concentration and other jobs which humans may find degrading. General Motors uses around 16,000 robots for tasks such as painting, welding, and assembly. Japan is the leader in using and producing robots in the world. In 1995, 700,000 robots were in use worldwide; over 500,000 of which were from Japan. Thus, 13
  • 15. Universiti Tunku Abdul Rahman from the number itself, it can be seen that it is very important to have AI in the robots to keep the productivity at a constant level. Besides the application of AI in heavy industries robots, AI also part of the crucial components in human-like robots such as famous Android Aiko and ASIMO. Both of the robots have the ability to recognize speech, voice, faces, motion, objects and also learn simple thing with the AI implemented in the software that operating them. Without AI, a robot will be doing a same thing everytime as programmed. The latest breakthrough in robotics AI happens when a relatively new company called AI Robotics based in Kobe, Japan has developed a female robot called „Lisa – The Perfect Woman‟ which equipped with RKS, “Recognition Krax System”, which allows for vocal, tactile and visual recognition. Lisa is able to recognize objects and persons and she can even differentiate between roses and tulips for example. Claimed by the creators, Lisa can cook a meal based on what is in the fridge using visual recognition. She also can go shopping, do household work or give a hydraulic massage, and she can also play chess and video games and even learn to do certain sports. Figure 6 & Figure 7 Android Aiko (Left) and Lisa (Right) 2.10 Theorem Proving Automated theorem proving is appealing due to the rigor and generality of logic. Because it is a formal system, logic lends itself to automation. A wide variety of problems can be attacked by representing the problem description and relevant background information as logical axioms and treating problem instances as theorems to be proved. This insight is the basis of work in automatic theorem proving and mathematical reasoning systems. 14
  • 16. Universiti Tunku Abdul Rahman Unfortunately, early efforts at writing theorem provers failed to develop a system that could consistently solve complicated problems. This was due to the ability of any reasonably complex logical system to generate an infinite number of provable theorems: without powerful techniques (heuristics) to guide their search, automated theorem provers proved large numbers of irrelevant theorems before stumbling onto the correct one. In response to this inefficiency, many argue that purely formal, syntactic methods of guiding search are inherently incapable of handling such a huge space and that the only alternative is to rely on the informal, ad hoc strategies that humans seem to use in solving problems. This is the approach underlying the development of expert systems (Chapter 8), and it has proved to be a fruitful one. Still, the appeal of reasoning based in formal mathematical logic is too strong to ignore. Many important problems such as the design and verification of logic circuits, verification of the correctness of computer programs, and control of complex systems seem to respond to such an approach. In addition, the theorem- proving community has enjoyed success in devising powerful solution heuristics that rely solely on an evaluation of the syntactic form of a logical expression, and as a result, reducing the complexity of the search space without resorting to the ad hoc techniques used by most human problem solvers. Another reason for the continued interest in automatic theorem provers is the realization that such a system does not have to be capable of independently solving extremely complex problems without human assistance. Many modern theorem provers function as intelligent assistants, letting humans perform the more demanding tasks of decomposing a large problem into subproblems and devising heuristics for searching the space of possible proofs. The theorem prover then performs the simpler but still demanding task of proving lemmas, verifying smaller conjectures, and completing the formal aspects of a proof outlined by its human associate. 15
  • 17. Universiti Tunku Abdul Rahman Chapter 3 Expert System 3.1 Introduction As mentioned previously, an expert system is actually an advisory system that embedded with human expertise which mainly used in solving particular types of problems. Expert systems were developed in the mid-1960s and have begun to emerge in the following decade. By 1980s, it has also started to be implemented in commercial field other than academic applications. Expert systems have being the most common applied AI technology up to nowadays. Expert systems can be used in various problem areas, such as interpretation systems, prediction systems, diagnostic systems, design systems, developing plans to achieve goal, comparing observations, debugging, repairing some diagnosed problems, instructing or correcting student performance and controlling the system behavior. Below are several types of expert systems: Categories Description Rule-based expert systems It is normally implemented in commercial field, its knowledge is presented as production rules (i.e. IF some conditions, THEN action). Frame-based expert systems The knowledge is presented as frames, which the knowledge of a particular object is organized in a special hierarchical structure. Hybrid expert systems There includes a combination of several knowledge representation approaches in this systems. Model-based expert systems Model being used in this system as reference in comparing with experimental subject. Systems classified by their This system will classify the nature of the problem nature and solve it by retrieving data according to the class of the problem. Ready-made expert systems Computer systems that are designed based on general use instead of the needs of particular users. Real-time expert systems It has to response as fast as possible by the time it is needed. Table 1 Types of expert systems 16
  • 18. Universiti Tunku Abdul Rahman The remainder of this assignment will be introducing the fundamentals of expert systems, which includes the basic ideas of expert systems and how it works. We will also briefly introduce the application of expert systems in the medical field. 3.2 Basic Concepts of Expert Systems In consequence of the fast growing technologies nowadays, experts are highly on demand in this competitive world. Limited experts in various fields such as engineering, has become a problem for them to stay in pace with the development. In response to this problem, knowledge lies within experts is needed to be captured. Expert systems in this case act as an advisory system or even can be used by experts as knowledgeable assistants. In building such expert systems, the knowledge of experts will be transferred into a computer system. It can then provide some advices even explanations to non-experts or novices. The basic concepts of expert systems include: expertise, experts, transferring expertise and explanation capability. Expertise Expertise, normally defined as a high level of knowledge or skill, but in expert systems we define it as a specific knowledge or skill acquired from training, reading and experience. It is the main element that is to be implemented in the expert systems, which enables non-experts or novices to solve particular type of problems. Experts The so-called expert is the person who possessed of expertise in their field and playing the most important role in problem solving and decision making. They are the main knowledge resources in building expert systems. To mimic the human experts, expert systems need to have the ability in solving problems, learn from experience, restructure knowledge and rebuild the rules where necessary. Transferring expertise Transferring expertise is the main objective of the expert systems, which transfer from expert to computer system and then to non-experts or novices. The transferring process basically involves knowledge acquisition, representation, 17
  • 19. Universiti Tunku Abdul Rahman inferencing and transfer to the user. Knowledge acquisition is the process that transfers the knowledge of experts to the computer system, which then stored in the system and represented in the computer. And knowledge inferencing is a unique feature of the expert systems which enables the systems to reason. After inferencing knowledge, finally it will transfer the solution to the user based on the rules and facts regarding to the problems. Explanation capability Expert systems are distinct from the conventional computer systems, which it is capable in explaining its advices or operations. This feature allows users to understand more on the advices provided and also enables the system to justify its own reasoning. Data, problems, questions Knowledge Base Human Knowledge Inference Experts Engineer Engine User Knowledge, concepts, solutions Figure 8 Process of transferring expertise 3.4 How Expert Systems Work Common components in Expert Systems There are few common components that might exist in the expert systems: Knowledge acquisition subsystem It is responsible in constructing and expanding the knowledge base by transferring the knowledge from all possible sources such as human experts, 18
  • 20. Universiti Tunku Abdul Rahman books, databases and graphical resources to the computer system. A knowledge engineer might be playing an important role in this process. Knowledge base Typically, the knowledge base is separated into facts and rules. Facts are something like problem situations and theories underneath, whereby rules are used to manage suitable knowledge in solving particular problems. Inference engine This component is the reasoning tool of the expert systems. It interprets on the information from knowledge base and blackboard, and provides a direction to the appropriate system‟s knowledge. Blackboard It is also known as the workplace of the expert systems, which mainly used in recording the specified problem (input) and also the intermediate hypotheses and decisions. User interface Expert systems are user-friendly, which its user interface provides a platform for user to communicate with the system in natural language. Explanation subsystem This subsystem provides the explanation capability to the expert systems. Knowledge refining system Refinement is essential to build a good expert system. With refinement, expert systems able to check on its own performance, make improvements and learn from experience. Human elements involved The main human elements that involved in expert systems are as follows: Experts Knowledge engineer Basically, a knowledge engineer is responsible in interacting with human experts to build the knowledge base. In building the expert systems, they may cooperate with other computerized systems to integrate it. User 19
  • 21. Universiti Tunku Abdul Rahman How it works? Basically, the operation of the expert systems can be categorized into 2 parts: development and consultation. In the first stage, development, activities that involved includes constructing the knowledge base, inference engine, blackboard, explanation facility and any other necessary software. These activities may be complex; therefore a tool called ES shell has been used to speed up the process. The ES shell includes all basic components of expert systems except the knowledge. Improvement can be done to the systems by rapid prototyping during their development. Rapid prototyping will represent the knowledge acquired in a better manner that allows quick inference process. After completed the construction stage, the system will be tested and validated, and then comes to the consultation part. Expert systems are well designed to be user-friendly with intuitive features. Hence, the user can easily input their problems and get advices and explanation in the consultation environment. During the consultation process, more questions may be asked and answered to reach a conclusion. The inference engine involve in the reasoning process whereby explanation facility provides explanations to the user. Inference Engine Knowledge Acquiring Subsystem User User Interface Blackboard Knowledge Base Explanation Subsystem Figure 9 Main components of expert systems and their interrelationship 20
  • 22. Universiti Tunku Abdul Rahman 3.5 Expert Systems in Medical Field Concerning medicine, there are a number of expert systems for different usage have been developed. Some of the well-known examples are as follows: Expert systems Description MYCIN For diagnosing bacteria that cause severe infections and recommending antibiotic dosage according to different patient‟s body weight HELP A complete knowledge based hospital information system deDombal`s Leeds For acute abdominal pain Abdominal Pain System Table 2 Lists of well-known expert systems in medical field Other than the above common medical expert systems, there is more expert systems have been developed recent years in assisting medical works. The development includes in: Acute care systems, Decision support systems, Education systems, Quality assurance and administration, Medical imaging, Drug administration, Laboratory systems. (Coiera, 1997) Categories Example application Acute care systems Coronary care admission, giving advices on the management of chest pain patients in the emergency room. Decision support Typical example is HELP system that has been mentioned systems above. Education systems Delivering knowledge on how to reduce risk of cancer. Quality assurance Monitoring patient clinical data for potential adverse drug effect. Medical imaging Automatic interpretation of medical imaging data such as Cardiac SPECT data. Laboratory systems Haematology analyzer Table 3 Application example in different categories of medical expert systems 21
  • 23. Universiti Tunku Abdul Rahman 3.6 Pros and Cons of Expert Systems Advantages of expert systems Expert systems have brought numerous benefits to the users: Productivity will increase as expert systems work much faster than human. And the output will also increase due to the reduction of workers needed which in turn reduce in costs. Scarce expertise can be captured. Minimize the employees training costs. Expert systems can be made in many copies where eliminate the needs of human experts to travel around and increase the accessibility of expertise. Expert systems can integrate knowledge from several experts. Expert systems can operate in hazardous environments and they will not affect by temper or tiredness because they do not have feelings. Limitations of expert systems Beside benefits stated above, there are also some problems and limitations in dealing with expert systems: It is not easy to gather knowledge for the computer systems. Situation assessment may vary from different experts in different conditions. Expert systems may make mistakes. There is no common sense being used in making decision. The construction of expert systems is complex and expensive. Expert systems will only work well in a narrow domain. Expert systems have no flexibility and ability to adapt to changing environment or gaining new knowledge without reprogramming. 3.7 Conclusion Expert systems are very beneficial computer systems which able to give crucial advices or opinions to non-experts by using their acquired knowledge. These systems are definitely not replacing the current available human experts but are assisting experts in dealing with their job or decision making process. Although 22
  • 24. Universiti Tunku Abdul Rahman expert systems are having a very wide range of applications in many fields, there are still some limitations and problems occur as we discussed in the previous section. Following the technology and market trend, hard work must be done on expert systems in solving these limitations for the systems to stay competitive. The future of expert systems may include the following: increasing system learning capabilities, using multiple sources of expertise, improving reasoning capabilities and combining few expert systems working together. Besides that, expert systems may also cooperate with other technology such as fuzzy logic, robotics, neural network and so forth. Neural network in this case could help easing the knowledge acquisition task in the construction of expert systems, which can cut down the cost and time in employing a knowledge engineer. Therefore, current expert systems still can be potentially improved. Various research topics that have great impact in improving expert systems are under investigation. 23
  • 25. Universiti Tunku Abdul Rahman Chapter 4 Conclusion We have attempted to define artificial intelligence through discussion of its major areas of research and application. This has shown that AI can be applied in intelligent problem solving, planning, and communication skills to a wide range of practical problems. Some of the common features in the application of AI in every fields including: 1. The use of computers to do reasoning, pattern recognition, learning, or some other form of inference. 2. The use of large amounts of domain-specific knowledge in solving problems. This is the basis of expert systems. 3. The purpose of using AI mainly to increase the productivity and decrease the workload of a human. As a conclusion, the application of AI has become very wide, and it is expected to expand even wider to other fields. It is predictable that in the future, some of the AI application will get combined and builds up an even more powerful and stronger AI system. Reference Turban, Efraim. (1992), Expert systems and applied artificial intelligence. Macmillan. Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, NJ: Prentice Hall. Wikipedia – Application of Artificial Intelligence .(2009), Retrieved June 20, 2009, from Wikipedia website: http://en.wikipedia.org/wiki/Applications_of_artificial_intelligence Machine learning. (2009). In Encyclopædia Britannica. Retrieved June 24, 2009, from Encyclopædia Britannica Online: http://www.britannica.com/EBchecked/topic/1116194/machine-learning AI Overview. (2008). Retrieved June 19, 2009 from AAAI.org website: http://www.aaai.org/AITopics/pmwiki/pmwiki.php/AITopics/AIOverview Microsoft Predicts The Future With Vista's SuperFetch. (2007). Retrieved June23 2009, from Information Week website: http://www.informationweek.com/news/windows/showArticle.jhtml?articleID=196902178 24