000			02460nam a22001817a 4500
082			_a004.019 _bD535I
100			_aDhulipalla, Nikhila _945347
245			_aThe Inner Self: Finding the Cause of Action, Thinking Over the Doings _cby Nikhila Dhulipalla
260			_aIIT Jodhpur _bDepartment of Computer Science and Technology _c2023
300			_axii, 37p. _bHB
520			_aThe concept of metacognition is a framework that allows an artificial intelligence system to think about what it thinks. This enables it to perform various tasks such as memory representation, retrieval, and mining. Complex constructs in metacognition are important factors that affect the development of artificial intelligence systems that can learn and use memory. Various metacognition and memory elements are used in designing and developing AI systems designed to perform missions. One of these is the ability to make a judgment about the amount of information it has available to complete a task. This is done through the Emergent Behavior concept, which allows a system to maintain its attention while performing the analytical process. The use of the genetic algorithm in engineering is regarded as an adaptive technique that can be used to solve complicated problems. This meta-heuristic approach is commonly used in hybrid computation. It utilizes mutation operators, selection, and crossover techniques to manage the system strategy. The genetic algorithm is derived from the concepts of natural selection and genetics. It can be used to improve the performance of a coverage framework by utilizing a random search method. These techniques are often used in statistical analysis to find a suitable solution to a given problem or prevent an error-prone strategy from being implemented in a search. They were developed from genetics or natural selection principles. The project aims to create a meta-cognitive phase that cognitive architectures may leverage to execute essential tasks using the Evolutionary Algorithm like Genetic Algorithm in MIDCA architecture. This is one of the first attempts to integrate a Genetic Algorithm into the metacognitive phase and reason out based on the energy consumption on a real-time residential dataset.
650			_aDepartment of Computer Science and Technology _945348
650			_aMetacognition _945349
650			_aMTech Theses _945350
700			_aWaghmare, Rahul G _945351
700			_aVatsa, Mayank _945352
942			_cTH
999			_c16571 _d16571