Technical Pub. Pune, - Artificial intelligence · 0 Reviews. What people are saying - Write a review. We haven't found any reviews in the usual places. Artificial Intelligence [Mrs. Neeta Deshpande] on portal7.info *FREE* Story time just got better with Prime Book Box, a subscription that delivers editorially. Artificial Intelligence for Pune University by Neeta Deshpande, , available at Book Depository with free delivery worldwide.

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Artificial Intelligence. Front Cover · Neeta Deshpande. Technical Publications, - pages. 3 Reviews. Definition, What is A.I? Foundation of A.I., History, . User Review - Flag as inappropriate. This is a very good book which i have gone through, explanation is given in very easy to understand. A lot of problems are. Definition, What is A.I? Foundation of A.I., History, Intelligent Agents, Agent architecture, A. I. Application (E Commerce & Medicine), A. I. Representation.

However, if the agent is not the only actor, it must periodically ascertain whether the world matches its predictions and it must change its plan as this becomes necessary, requiring the agent to reason under uncertainty. Notes Natural language processing NLP gives machines the ability to read and understand the languages that humans speak. A sufficiently powerful natural language processing system would enable natural language user interfaces and the acquisition of knowledge directly from humanwritten sources, such as Internet texts. Figure 1. Some straightforward applications of natural language processing include information retrieval or text mining and machine translation.

Introduction to Artificial Intelligence presents an introduction to the science of reasoning processes in computers, and the research approaches and results of the past two decades. You'll find lucid, easy-to-read coverage of problem-solving methods, representation and models, game playing, automated understanding of natural languages, heuristic search theory, robot systems, heuristic scene analysis and specific artificial-intelligence accomplishments. Related subjects are also included: predicate-calculus theorem proving, machine architecture, psychological simulation, automatic programming, novel software techniques, industrial automation and much more.

A supplementary section updates the original book with major research from the decade Abundant illustrations, diagrams and photographs enhance the text, and challenging practice exercises at the end of each chapter test the student's grasp of each subject. The combination of introductory and advanced material makes Introduction to Artificial Intelligence ideal for both the layman and the student of mathematics and computer science.

For anyone interested in the nature of thought, it will inspire visions of what computer technology might produce tomorrow.

Unsupervised learning includes both classification and numerical regression. Voice recognition VR is also known as automatic speech recognition or speech to text.

Its principles include the data structures used and knowledge representation, the algorithms needed to apply the knowledge, and language and programming techniques used in their implementation. Neural networks typically take a vector of input values and produce a vector of output values.

Inside, they train weights of neurons.

Neural networks use supervised learning, in which inputs and outputs are known and the goal is to build a representation of a function that will approximate the input to output mapping.

The approximate function f is typically smooth: for x close to x, we will expect that f x is close to f x. Function approximation serves two purposes: Size: the representation of the approximate function can be significantly smaller than the true function. Generalization: the approximate function can be used on inputs for which we do not know the value of the function. We do not already know the output paths.

But if we are able to compute a path given start, goal , then we already know the function f, so why bother approximating it?

The only potential benefit would be in reducing the size of the representation of f. The representation of f is a fairly simple algorithm, which takes little space, so I dont think thats useful either. In addition, neural networks produce a fixed-size output, whereas paths are variable sized.

Notes Genetic Algorithms: Genetic Algorithms allow to explore a space of parameters to find solutions that score well according to a fitness function.

They are a way to implement function optimization: given a function g x where x is typically a vector of parameter values , find the value of x that maximizes or minimizes g x. This is an unsupervised learning problemthe right answer is not known beforehand.

For path finding, given a starting position and a goal, x is the path between the two and g x is the cost of that path. Simple optimization approaches like hill-climbing will change x in ways that increase g x.

Unfortunately in some problems, we reach local maxima, values of x for which no nearby x has a greater value of g, but some faraway value of x is better.

Genetic algorithms improve upon hill-climbing by maintaining multiple x, and using evolution-inspired approaches like mutation and cross-over to alter x.

Both hill-climbing and genetic algorithms can be used to learn the best value of x. Genetic Programming takes genetic algorithms a step further, and treats programs as the parameters. For example, we would breed path finding algorithms instead of paths, and your fitness function would rate each algorithm based on how well it does.

For path finding, we already have a good algorithm and we do not need to evolve a new one. It may be that as with neural networks, genetic algorithms can be applied to some portion of the path finding problem. Reinforcement Learning: Like genetic algorithms, Reinforcement Learning is an unsupervised learning problem.

However, unlike genetic algorithms, agents can learn during their lifetimes; its not necessary to wait to see if they live or die. Also, its possible for multiple agents experiencing different things to share what theyve learned.

In reinforcement learning, every state can be evaluated and its reward or punishment is propagated back to mark all the choices that were made leading up to that state. One of the key advantages of reinforcement learning and genetic algorithms over simpler approaches is that there is a choice made between exploring new things and exploiting the information learned so far.

In genetic algorithms, the exploration via mutation; in reinforcement learning, the exploration is via explicitly allowing the probability of choosing new actions. As with genetic algorithms, we dont believe reinforcement learning should be used for the path finding problem itself, but instead as a guide for teaching agents how to behave in the game world.

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