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Fundamentals of Artificial Intelligence
History, motivation and need of AI, production systems, search space, hill climbing, best first search, heuristic search, A* and AO* search techniques.
Complete unit-wise study material for AL304 Artificial Intelligence for RGPV Artificial Intelligence and Machine Learning III Semester students.
Study all five units of Artificial Intelligence covering AI fundamentals, knowledge representation, probabilistic reasoning, game playing, NLP and expert systems.
History, motivation and need of AI, production systems, search space, hill climbing, best first search, heuristic search, A* and AO* search techniques.
Problems in knowledge representation, propositional and predicate logic, resolution, refutation, deduction, theorem proving, inference and monotonic and non-monotonic reasoning.
Probabilistic reasoning, Bayes' theorem, semantic networks, scripts, schemas, frames, conceptual dependency and forward and backward reasoning.
Minimax procedure, alpha-beta cut-offs, planning, block world problem in robotics, natural language processing, components of NLP and application of NLP in expert systems.
Expert system characteristics, requirements, components, capabilities, inference engine, forward and backward chaining, limitations, development environment and benefits.
Complete unit-wise syllabus for RGPV Artificial Intelligence and Machine Learning III Semester.
Fundamental of Artificial Intelligence, history, motivation and need of AI, Production systems, Characteristics of production systems, goals and contribution of AI to modern technology, search space, different search techniques: Hill Climbing, Best First Search, heuristic search algorithm, A* and AO* search techniques etc.
Knowledge Representation, Problems in representing knowledge, knowledge representation using propositional and predicate logic, comparison of propositional and predicate logic, Resolution, refutation, deduction, theorem proving, inferencing, monotonic and non-monotonic reasoning.
Probabilistic reasoning, Baye's theorem, semantic networks, scripts, schemas, frames, conceptual dependency, forward and backward reasoning.
Game playing techniques like minimax procedure, alpha-beta cut-offs etc, planning, Study of the block world problem in robotics, Introduction to understanding, natural language processing (NLP), Components of NLP, application of NLP to design expert systems.
Expert systems (ES) and its Characteristics, requirements of ES, components and capability of expert systems, Inference Engine Forward & backward Chaining, Expert Systems Limitation, Expert System Development Environment, technology, Benefits of Expert Systems.
AL304 Artificial Intelligence introduces students to the core concepts and methods used in intelligent systems, including searching, reasoning, knowledge representation, planning and expert systems.
Students can access all five units in one place and prepare according to the RGPV syllabus using unit-wise handwritten notes.
Select any unit and start your Artificial Intelligence preparation.
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