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SEA Side Software Engineering Annotations AAnnotation 7 Artificial Intelligence One hour presentation to inform you of new techniques and practices in software development Professor Sara Stoecklin Director of Software Engineering Panama City Florida State University Computer Science sstoecklin mail pc fsu edu stoeckli cs fsu edu 850 522 2091 850 522 2023 Ex 182 Artificial 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 Perception Problem 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 Search Often 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 Searches Breadth First Search Depth First Search Heuristic Search Generate and Test Technique Simple Hill Climbing Steepest Ascent Hill Climbing Best First Search A Search Genetic Searches Knowledge Representation Intelligent 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 Multi valued Logic Language First Order Logic Propositional Logic Pseudo Boolean Agents KNOWLEDGE REPRESENTATION Modeling Hierarchies Incomplete Knowledge Model Clustering Solution Structure Supermodels Symmetry Semantic Nets The 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 Logic The 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 logics Natural Language Processing Language 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 Systems Most 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 Networks Expert Systems most 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 rules Rule Based Systems Consists 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 DRIVEN Backward 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 DRIVEN Uncertainty in Rules Rules 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 grayness a degree of rightness or wrong ness It is for this that Fuzzy Logic was developed Components of Fuzzy Logic Systems Arithmetic Logic Unit to interpret relationships such as etc Set of Knowns To compare an input to Membership Function Calculation that determines how closely the input can be matched to a set of knowns Benefits Provides information


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