Applying Computational Intelligence Volume 5969 || Artificial vs. Computational Intelligence

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<ul><li><p>Chapter 1</p><p>Artificial vs. Computational Intelligence</p><p>There are three great events in history. One, the creationof the universe. Two, the appearance of life. The thirdone, which I think is equal in importance, is the appear-ance of Artificial Intelligence.</p><p>Edward Fredkin</p><p>Artificial Intelligence is not a science.Maurizio Matteuzzi</p><p>Hardly any area of research has created such wide publicity and controversy as</p><p>Artificial Intelligence (AI). To illustrate, the abbreviation AI is one of the few</p><p>scientific buzzwords accepted in contemporary English language. Artificial intelli-</p><p>gence has successfully penetrated Hollywood and even grabbed the attention of star</p><p>director Steven Spielberg. His blockbuster movie AI spread the artistic vision of theconcept to millions.</p><p>Considering the enormous popularity of AI, the reader may ask the logical</p><p>question: Why would we need a different name? The first chapter objective is to</p><p>answer this question by defining the fields of artificial and computational intelli-</p><p>gence and clarifying their differences. In both cases, the focus will be on the</p><p>application capabilities of these broad research areas. The chapter introduces the</p><p>most important techniques and application areas of classical AI.1 The key methods</p><p>and application issues of computational intelligence are discussed in detail in the</p><p>rest of the book. If a reader is not interested in the technical nature of AI, she/he can</p><p>skip Sect. 1.1.</p><p>1the state of AI before the appearance of computational intelligence.</p><p>A.K. Kordon, Applying Computational Intelligence,DOI 10.1007/978-3-540-69913-2_1,# Springer-Verlag Berlin Heidelberg 2010</p><p>3</p></li><li><p>1.1 Artificial Intelligence: The Pioneer</p><p>The enormous popularity of AI has its price. The perception of the field from</p><p>different research communities is very diverse and the opinions about its scientific</p><p>importance vary from one extreme of treating AI as low as contemporary alchemyto another extreme of glorifying its impact as one of the key events in history. The</p><p>reason for this controversy is the complex nature of the key subject of AI human</p><p>intelligence. AI research efforts are focused on analyzing human intelligence using</p><p>the methods of computer science, mathematics, and engineering. The classical</p><p>definition of AI, according to The Handbook of Artificial Intelligence is as follows:</p><p>Artificial Intelligence is the part of computer science concerned with designing intelligent</p><p>computer systems, that is, systems that exhibit the characteristics we associate with intelli-</p><p>gence in human behavior understanding language, learning reasoning, solving problems,</p><p>and so on.2</p><p>Meanwhile, human intelligence is a very active research area of several human-</p><p>ity-based scientific communities, such as neurophysiology, psychology, linguistics,</p><p>sociology, and philosophy, to name a few. Unfortunately, the necessary collabora-</p><p>tion between the computer science-based and humanity-based research camps is</p><p>often replaced by the fierce exchange of scientific artillery fire, which does not</p><p>resolve the issues but increases the level of confusion. There is a common percep-</p><p>tion inside and outside the academic community that the research efforts on</p><p>analyzing intelligence have not been handled in a very intelligent way.</p><p>1.1.1 Practical Definition of Applied Artificial Intelligence</p><p>As a result of the academic schisms, different definitions about the nature of human</p><p>and artificial intelligence are fighting for acceptance from the research community</p><p>at large. The interested reader could find them in the recommended references at the</p><p>end of the chapter. Since our focus is on value creation, we will pay attention to</p><p>those features of AI that are important from an implementation point of view.</p><p>The accepted birth of AI is the famous two-month workshop at Dartmouth</p><p>College during the summer of 1956 when ten founding fathers defined the initial</p><p>scientific directions in this new research area imitating human reasoning, under-</p><p>standing language (machine translation), finding a General Problem Solver, etc. For</p><p>many reasons, however, these generic directions did not produce the expected</p><p>results in the next decade even with very generous funding from several govern-</p><p>mental agencies in different countries. One of the lessons learned was that the initial</p><p>strategy of neglecting domain knowledge (the so-called weak methods for buildinggeneral-purpose solutions valid for any problems) failed. Research efforts have been</p><p>2A. Barr and E. Feigenbaum, The Handbook of Artificial Intelligence, Morgan Kaufmann, 1981.</p><p>4 1 Artificial vs. Computational Intelligence</p></li><li><p>redirected to developing reasoning mechanisms based on narrow areas of expertise.</p><p>It turns out that this approach not only delivered successful scientific results in the</p><p>early 1970s but opened the door to numerous practical applications in the 1980s.</p><p>From the practical point of view, the most valuable feature of human intelligence</p><p>is the ability to make correct predictions about the future based on all available data</p><p>from the changing environment. Usually, these predictions are specific and limited</p><p>to some related area of knowledge. From that perspective, we define applied AI as asystem of methods and infrastructure to mimic human intelligence by representingavailable domain knowledge and inference mechanisms for solving domain-specificproblems. In a very elementary way we can describe this narrow-focused definitionof AI as Put the expert in the box (computer), and we visualize it in Fig. 1.1.</p><p>In contrast to the initial generic-type of AI research, which had difficulties in</p><p>identifying the potential for value creation, the defined applied AI has several</p><p>sources for profit generation. The first one is improving the productivity of any</p><p>process by operating continuously at the top experts level. The second source of</p><p>potential value creation is reducing the risks of decision-making through knowl-</p><p>edge consolidation of the best and the brightest in the field. The third source of</p><p>profit from using applied AI is by conserving the existing domain knowledge</p><p>independently when the expert leaves the organization.</p><p>The definition of success for applied AI is a computerized solution that responds</p><p>to different situations like the domain experts, imitates their reasoning and makes</p><p>predictions which are indistinguishable from the experts. The practical AI dream is</p><p>a machine clone of the domain-related brain of the expert.</p><p>1.1.2 Key Practical Artificial Intelligence Approaches</p><p>There are several methods to clone an expert brain and the most important are</p><p>shown in Fig. 1.2. An expert system captures expert knowledge in the computer</p><p>Fig. 1.1 The icon of appliedAI Put the expert in the</p><p>computer</p><p>1.1 Artificial Intelligence: The Pioneer 5</p></li><li><p>using rules and frames. Expert reasoning is mimicked by two key inference</p><p>mechanisms goal-driven (backward-chaining) and event-driven (forward chain-</p><p>ing). If expert knowledge can be represented by different cases, it could be</p><p>structured in a special type of indexed inference, called case-based reasoning.</p><p>Knowledge management is a systematic way of knowledge acquisition, representa-</p><p>tion, and implementation. These key applied AI methods are discussed in more</p><p>detail below.</p><p> Expert Systems (Rule-Based and Frame-Based)</p><p>Rule-Based Expert Systems</p><p>One possible way to represent human knowledge in a computer is by rules. A rule</p><p>expresses a programmatic response to a set of conditions. A rule contains text and a</p><p>set of attributes. When you create a new rule, you enter a two-part statement in its</p><p>text. The first part, called the antecedent, tests for a condition. The second part,</p><p>called the consequent, specifies the actions to take when the condition returns a</p><p>value of true. This is an example of the text of a rule:</p><p>If the level of any tank = 0 then conclude that the status of the tank is empty</p><p>antecedent</p><p>consequent</p><p>A rule-based expert system consists of a set of independent or related rules. The</p><p>performance and the value creation of the rule-based expert system strongly depend</p><p>on the quality of the defined rules. The domain expert plays a critical role in this</p><p>Expert Systems</p><p>Inference Mechanisms</p><p>Key Artificial IntelligenceApproaches</p><p>Knowledge Management</p><p>Case Based Reasoning</p><p>Rule Based</p><p>Frame Based</p><p>BackwardChaining</p><p>ForwardChaining</p><p>Fig. 1.2 Key approaches of applied AI</p><p>6 1 Artificial vs. Computational Intelligence</p></li><li><p>process. Rule-based expert systems can only be as good as the domain experts they</p><p>are programmed to mimic.</p><p>Frame-Based Expert Systems</p><p>Another way to represent domain knowledge is by frames. This method was</p><p>introduced by one of the AI founding fathers, Marvin Minsky, in the 1970s and</p><p>was pushed further by the emerging field of object-oriented programming. A frame</p><p>is a type of knowledge representation in which a group of attributes describes a</p><p>given object. Each attribute is stored in a slot which may contain default values. An</p><p>example of a typical object in a manufacturing monitoring system is a process</p><p>variable with its attributes. Object attributes include all necessary information</p><p>for representing this entity, including the data server name, tag name, process</p><p>variable value, update interval, validity interval of the data, history keeping speci-</p><p>fication, etc.</p><p>A unique and very powerful feature of frame-based expert systems is their</p><p>ability to capture generic knowledge at a high abstract level by defining a hierarchy</p><p>of classes. For example, the class hierarchy of a manufacturing monitoring system</p><p>includes generic classes, such as process unit, process equipment, process variable,</p><p>process model, etc. As generic-type objects, classes have attributes, which can be</p><p>user-defined or inherited from the upper classes in the hierarchy. For example, the</p><p>class process equipment includes inherited attributes, such as unit name and unit</p><p>location from the upper class process unit, and user-defined attributes, such as</p><p>dimensions, inlet process variables, outlet process variables, control loops, etc.</p><p>Classes may have associated methods, which define the operations characteristic</p><p>of each class. Polymorphism allows methods representing generic operations to be</p><p>implemented in class-specific ways. Code that invokes a method needs only to</p><p>know the name of the object and the methods signature. For example, the process</p><p>equipment class may include the methods for start-up and shut-down sequences.</p><p>The role of the domain-knowledge expert is to define the class hierarchy in the</p><p>most effective way. The biggest advantage of frame-based expert systems is that</p><p>once an object (or class of objects) is defined, the work is immediately reusable.</p><p>Any object or group of objects can be cloned over and over again. Each cloned copy</p><p>inherits all of the properties and behaviors assigned to the original object(s). It is</p><p>possible to group objects, rules, and procedures into library modules that can be</p><p>shared by other applications.</p><p> Inference Mechanisms</p><p>The next key topic related to applied AI is the nature of inference of the artificial</p><p>expert. It addresses how the defined rules are selected and fired. At the core of this</p><p>topic is the inference engine. An inference engine is a computer program which</p><p>1.1 Artificial Intelligence: The Pioneer 7</p></li><li><p>processes the contents of a knowledge base by applying search strategies to the</p><p>contents and driving conclusions. The most important techniques used for inference</p><p>are backward chaining and forward chaining.</p><p> Backward Chaining</p><p>The first inference mechanism, backward chaining, is based on goal-driven</p><p>reasoning. Backward chaining begins by attempting to seek a value or values for</p><p>a known goal (usually a possible solution, defined by the experts). This solution is</p><p>usually a consequent in the rules. The process continues by seeking values on which</p><p>the goal depends. If an appropriate rule is found and its antecedent section fits the</p><p>available data, then it is assumed that the goal is proved and the rule is fired. This</p><p>inference strategy is commonly used in diagnostic expert systems.</p><p> Forward Chaining</p><p>The second inference mechanism, forward chaining, is based on data-driven</p><p>reasoning. Forward chaining begins by seeking eligible rules which are true for</p><p>known data values. Processing continues by seeking additional rules which can be</p><p>processed based on any additional data which has been inferred from a previously</p><p>fired rule or a goal is reached. This inference strategy is commonly used in</p><p>planning expert systems. It is a technique for gathering information and then</p><p>using it for inference. The disadvantage is that this type of inference is not related</p><p>to the defined goal. As a result, many rules could be fired that have no relationship</p><p>with the objective.</p><p> Case-Based Reasoning</p><p>Reasoning by analogy is an important technique in human intelligence. Very often</p><p>domain experts condense their knowledge on a set of past cases of previously</p><p>solved problems. When a new problem appears, they try to find a solution by</p><p>looking at how close it is to similar cases they have already handled. Case-based</p><p>reasoning is an AI approach that mimics this behavior.</p><p>Presenting domain knowledge by cases has several advantages. First, case-based</p><p>reasoning doesnt require a causal model or deep process understanding. Second,</p><p>the technique gives shortcuts to reasoning, the capability to avoid past errors, and</p><p>the capability to focus on the most important features of a problem. Third, the</p><p>knowledge acquisition process is much easier and less expensive than the rule- or</p><p>frame-based methods. In practice, cases require minimum debugging of the inter-</p><p>actions between them, unlike rule-based systems.</p><p>8 1 Artificial vs. Computational Intelligence</p></li><li><p>Case-based reasoning incorporates a six-step process:</p><p>Step 1: Accepting a new experience and analyzing it to retrieve relevant cases</p><p>from the domain experts.</p><p>Step 2: Selecting a set of best cases from which to define a solution or interpre-</p><p>tation for the problem case.</p><p>Step 3: Deriving a solution or interpretation complete with supporting arguments</p><p>or implementation details.</p><p>Step 4: Testing the solution or interpretation and assessing its strengths, weak-</p><p>nesses, generality, etc.</p><p>Step 5. Executing the solution or interpretation in practical situations and</p><p>analyzing the feedback.</p><p>Step 6: Storing the newly solved or interpreted case into case memory and</p><p>appropriately adjusting indices and other mechanisms.</p><p>When is it appropriate to apply case-based reasoning? One clear example is a</p><p>system that is based onwell-defined distinct entities (parts) supported by a rich history</p><p>of cases. In this instance representing the domain knowledge by specific examples is a</p><p>valid option that could significantly reduce the development cost relative to classical</p><p>knowledge management. Another example is a system of a very difficult problem</p><p>which requires very expensive development. In this case an approximate low-cost</p><p>solution based on case-based reasoning is better than none at all.</p><p> K...</p></li></ul>