PowerPoint PresentationSlide 2Slide 3Slide 4Slide 5Slide 6Slide 7Slide 8Slide 9Slide 10Slide 11Slide 12Slide 13Slide 14Slide 15Slide 16Slide 17Slide 18Slide 19Slide 20Slide 21Slide 22Slide 23Slide 24SEA Side Software Engineering Annotations•AAnnotation 7: Artificial IntelligenceOne hour presentation to inform you of new techniques and practices in software development. Professor Sara StoecklinDirector of Software Engineering- Panama CityFlorida State University – Computer [email protected]@cs.fsu.edu850-522-2091850-522-2023 Ex 182Artificial intelligence (AI) is a broad field, and means different things to different people. The field of Artificial Intelligence (AI) is concerned both with modeling human intelligence and with solving complex problems not solvable by simple or analytic procedures.For instance, a major goal of AI is construction of an intelligent robot, capable of perceiving, acting, comprehending, reasoning, and learning in complex environments.The AI field consists of six related areas: •Problem solving & search •Knowledge Representation •Natural Language Processing (NLP) •Reasoning Systems •Vision & PerceptionProblem Solving & Search A fundamental technique in AI is to encode a problem as a state space in which solutions are goal states in that space. Thus, problem solving can be viewed as state space search. To search large, combinatorial state spaces, knowledge (e.g. heuristics) and planning are required.SearchOften there is no direct way to find a solution to some problem. However, you do know how to generate possibilities. For example, in solving a puzzle you might know all the possible moves, but not the sequence that would lead to a solution. When working out how to get somewhere you might know all the roads/buses/trains, just not the best route to get you to your destination quickly. Developing good ways to search through these possibilities for a good solution is therefore vital. Brute force techniques, where you generate and try out every possible solution may work, but are often very inefficient, as there are just too many possibilities to try. Heuristic techniques are often better, where you only try the options which you think (based on your current best guess) are most likely to lead to a good solution.SearchesBreadth First SearchDepth First SearchHeuristic Search Generate and Test TechniqueSimple Hill ClimbingSteepest-Ascent Hill Climbing Best First Search A* Search Genetic SearchesKnowledge RepresentationIntelligent behavior often requires knowledge. For example, language comprehension requires encoding the meanings of words and how they are combined. Techniques for representing knowledge include use of semantic networks, logic, and neural networks.KNOWLEDGEREPRESENTATIONLanguageModelingSolution StructureMulti-valued LogicFirst Order LogicPropositional LogicPseudo-BooleanAgentsHierarchiesIncomplete KnowledgeModel ClusteringSupermodelsSymmetrySemantic NetsThe simplest kind of structured KE is the semantic net originally developed in the early 1960s to represent the meaning of English words. A semantic net is really just a graph, where the nodes in the graph represent concepts, and the arcs represent binary relationships between concepts. Predicate LogicThe most important knowledge representation language is arguably predicate logic (or strictly, first order predicate logic - there are lots of other logics out there to distinguish between). It is a well-defined syntax, semantics and rules of inference. …. Other logicsNatural Language ProcessingLanguage is the major medium for communicating thought and knowledge. NLP is concerned with mappings between language and thought, how language skills are learned, and how knowledge is acquired through language (e.g. reading). Really understanding a single sentence requires extensive knowledge both of language and of the context. For example They can fish can only be interpreted reasonably if you know the context, some ones job (canning fish) or recreational activity.Reasoning SystemsMost human reasoning occurs in task/domains with uncertain, ill-defined and incomplete knowledge. Reasoning in such domains requires techniques such as use of default, probabilistic, and non-monotonic logics. •Expert Systems •Fuzzy Logic •Case-Based Reasoning •Neural NetworksExpert Systemsmost common application utilized today employing the knowledge of an "expert" to solve problems or make decisions. Components of Expert Systems•Expert in the Field - to establish knowledge base on topic •Knowledge Engineer - Interviews experts and establishes (Heuristic) rules in an IF-THEN manner •Knowledge Base - Information stored on the computer about subject matter •Inference Engine - Compares inputs to known set of rules •Benefits•Provides information quicker than its human counterparts •Utilizes standards to diagnose relatively consistent problems •Limitations•Systems are not flexible changing the knowledge base can be cumbersome •Not applicable for problems that don't follow the rulesRule-Based SystemsConsists of IF-THEN rules, facts, and some interpreter (chainer) Two basic types: forward chaining and backward chaining systems. Forward chaining systems start with the initial facts, and keep using the rules to draw new conclusions (or take certain actions) given those facts. DATA DRIVENBackward chaining systems start with some hypothesis (or goal) you are trying to prove, and keep looking for rules that would allow you to conclude that hypothesis to be true, perhaps setting new subgoals to prove as you go. GOAL DRIVENUncertainty in RulesRules look pretty much like logical implications.In practice you rarely conclude things with absolute certainty. Usually we want to say things like ``If Alison is tired then there's quite a good chance that she'll be in a bad mood''. To allow for this sort of reasoning in rule-based systems we often add certainty values to a rule, and attach certainties to any new conclusions. We might conclude that Alison is probably in a bad mood (maybe with certainty 0.6). The approaches used are generally loosely based on probability theory, but are much less rigorous, aiming just for a good guess rather than precise probabilities.You can tune your expert system with these certainty factors.Fuzzy Logic Problems we encounter in our daily work and home environments frequently lack a complete black or white answer. Instead there’s a level of