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Throughout the years, Artificial Intelligence (AI) has made breakthroughs in nearly every industry, and the automobile world is no different. In fact, just last year, a survey on Forbes showed that 10% of auto companies have been using AI at scale. The same survey also noted that 80% of companies
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Alpha Beta Pruning: In Minimax Procedure, it seems as the static evaluator must be used on each leaf node. Fortunately, there is a procedure that reduces both the tree branches that must be generated and the number of evaluations. This procedure is called Alpha Beta pruning which “prunes” the tree
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Adversarial Search: Up until now all the searches that we have studied there were only one person or agent searching the solution space to find the goal or the solution. In many applications, there might be multiple agents or persons searching for solutions in the same solution space. Such scenarios
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We will be covering: * Best-first Search * Greedy Best First Search Algorithm * A* The best first search considers all the open nodes so far and selects the best amongst them. The following sequence of diagrams will show you how a best-first search procedure works in a search tree. • We start with
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So far we have looked at uninformed and informed searches. Both have their advantages and disadvantages. But one thing that lacks in both is that whenever they find a solution they immediately stop. They never consider that there might be more than one solution to the problem and the solution
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Heuristically Informed Searches: So far we have looked into procedures that search the search space in an uninformed manner. Such procedures are usually costly with respect to either time, space or both. We now focus on a few techniques that search the solution space in an informed manner using something
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The algorithm we studied in the previous lecture was a backtracking algorithm to search for a goal in a given graph. There are some other graph search algorithms which are more flexible. • Depth First Search • Breadth-First Search • Best First Search (will be discussed later) Depth First Search: In addition, to
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Historically many people link intelligence with the capability to solve a problem. Problem-solving is one of the factors that demonstrate intelligence. The classical approach that is used is “hit and trial” that check for various solutions to that problem. We use this approach to solve small problems. For example: Consider
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AI system is composed of an agent and its environment. What are Agent and Environment? An agent is anything that can perceive its environment through sensors and acts upon that environment through effectors. * A human agent has sensory organs such as eyes, ears, nose, tongue, and skin parallel to the
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Artificial Intelligence: Intelligence is an intangible part of our brain which is a combination of Reasoning, learning, problem-solving perception, language understanding, etc. To achieve the above factors for a machine or software Artificial Intelligence requires the following discipline: * Mathematics * Biology * Psychology * Sociology * Computer Science * Neurons Study * Statistics Artificial Intelligence is