Artificial Intelligence (AI) Models PDF

21CSC206T-Artificial Intelligence UNIT - 1 Introduction to AI 1  Topics Introduction to AI, AI techniques Problem - solving with AI AI Models Data acquisition and learning aspects in AI Problem solving-Problem solving process Form...

21CSC206T-Artificial Intelligence UNIT - 1 Introduction to AI 1  Topics Introduction to AI, AI techniques Problem - solving with AI AI Models Data acquisition and learning aspects in AI Problem solving-Problem solving process Formulating problems Problem types and characteristics Problem space and search Toy Problems–Tic-tac-toe problems Missionaries and Cannibals Problem Real World Problem–Travelling Salesman Problem 2 AI Models 3 AI Models Dunker introduced 'maze hypothesis' as a part of the psychological theory. In this particular hypothesis, the creative and intelligent tasks handled by human beings are modelled like a set of maze of paths from an initial node to a certain or resultant node. 4 AI Models Human at any point of time analyses maze; for choices, we could find those which can lead to goal. These choices and maze-based approach can help in solving many multialternative solution problems. 5 AI Models Effective application of logic theory machines is found very useful in general problem solving, even this is found very useful for a wide spectrum of problems like chess problem. Chess can be viewed as a controlled environment in which computer is given a situation and a goal. 6 AI Models Knowledge – based AI Application Model model building building Discover Mapping Discover relationship Complexity 7 AI Models Two aspects that could be viewed from chess program were knowledge-based search and knowledge acquisition and representation. Models used for applications like chess programs were not effective for the other applications. 8 AI Models A typical chess scenario is given in Figure. This is a much complex scenario than tic-tac-toe, but is still constrained. The chess program provided a sort of background for AI research. 9 AI Models The advent of natural language processing and the need for man-machine dialogue made it more evident that the models used so far had their own limitations. Then, the formal models were proposed to solve AI problems. 10 AI Models Semiotic Models: 11 AI Models Semiotic Models: These models are based on sign processes or signification and communication. The process of carrying meaning depends on codes. Semioticians classify signs or sign systems in relation to the problem. This meaning assignment and mapping process depends on the use of codes based on individual sounds or letters that humans use to form words or movements. In computers, these signs are determined for a logical sequence. 12 AI Models Statistical Models: Statistical models refer to representation and formalisation of relationships through statistical techniques. Most of the AI problems can be represented as statistical or pattern matching problems. Various learning models from AI perspective are based on statistics. The historical data is used here in decision-making. Statistical model employs probabilistic approaches and is typically a collection of probability density functions and distribution functions. 13 Data acquisition and learning aspects in AI 14