Advanced Artificial Intelligence Homework Assignments Questions help tutor
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Online Advanced Artificial Intelligence Assignment help experts with years of experience in the academic field as a professor are helping students online at Undergraduate , graduate & the research level .Our tutors are providing online assistance related to various topics like Representing and reasoning about objects, relations, events, actions, time, and space;, Predicate logic, situation calculus, description logics, reasoning with defaults,, Reasoning about knowledge.
The Artificial intelligence is the field of computer science which is used for developing intelligent machine and captures or analyze the human intelligence through artificial means & techniques for realizing intelligent behaviour.It makes computer controlled robot that behave like intellligent human.It involves the acquisition of information and rules for using the information ,rules to reach approximate and self-correction.
Important topics included in Artificial intelligence:
- Logic Foundation of Artificial Intelligence ,Constraint Reasoning ,Qualitative Reasoning ,Case-Based Reasoning
- Probabilistic Reasoning ,Inductive Learning ,Support Vector Machine ,Explanation-Based Learning
- Reinforcement Learning ,Rough Set ,Association Rules ,Evolutionary Computation ,Distributed Intelligence
Some of the homework help topics include:
- Approaches of AI
- Intelligent agents: reactive, deliberative, goal-driven, utility-driven, and learning agents
- Artificial Intelligence programming techniques
Generally topics like Planning: planning as search, partial order planning,, Construction and use of planning graphs, Representing and Reasoning with Uncertain Knowledge: probability,, Connection to logic are considered very complex & an expert help is required in order to solve the assignments based on topics like independence, Bayes rule, bayesian networks,, Probabilistic inference.
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Help for complex topics like:
- Problem-solving through Search,Evolutionary search algorithms ,Reasoning, Knowledge Representation,Ontologies, foundations of knowledge representation and reasoning
- Learning nearest neighbor, naive Bayes, and decision tree classifiers,Q-learning for learning action policies, applications.,Forward and backward
- state-space, blind, heuristic, problem-reduction,A, A*, AO*, minimax, constraint propagation, neural, stochastic, Decision-Making: basics of utility theory
- Decision theory, sequential decision problems, Machine Learning and Knowledge Acquisition, Learning from memorization & the assignment help on these topics is really helpful if you are struggling with the complex problems.
- Artifcial intelligence and agents, Agent architectures and hierarchical control, States and searching, Features and constraints, Propositions and inference
- Reasoning under uncertainty, Learning overview and supervised learning, Planning, Reasoning about individuals and relations.,,,Ontologies and knowledge-based systems
- Agents and rationality, Problem solving and search, Uninformed search, Informed search, Local search and genetic search, Adversarial search, Constraint satisfaction
- Logical agents, First-order logic.,First-order logic: inference, Planning, Graph planning and planning with resources, Real-world planning, Uncertainty, Continuous probability
- Decision analysis, Making complex decisions, Intro to learning & decision trees, Linear regression and neural networks, Applications and perspectives.
- Quantifying Uncertainty, Probabilistic Reasoning, Making Simple Decisions, Making Complex Decisions, Probabilistic Models, Reinforcement Learning
- Solving Problems by Searching, Beyond Classical Search, Adversarial Search, Logical Agents, First-Order Logic, Inference in First-Order Logic, Probabilistic Reasoning over Time
- Agents, Agents.,Problem Spaces and Blind Search, Search algorithms: Cost and Heuristics, Search Algorithms, Adversarial Search Minimax
Advanced Artificial Intelligence includes:
- Adversarial Search Alpha-Beta, Adversarial Search Expectimax, Markov Decision Processes, Reinforcement Learning, Uncertainty, Probablistic Inference
- Markov Models, Hidden Markov Models, Applications: Robotics, Bayesian Networks, Bayesian Networks Independence.,Bayesian Networks Inference
- Bayesian Networks Samplin, Machine Learning Naive Baye, Machine Learning Perceptron, Logic, Logic, Applications, Logical rationality , Economic rationality
- Psychological rationality , Representing knowledge , Knowledge acquisition , Consciousness and personhood , Deliberation and argumentation , Decision-model construction , Planning , Mind, matter, and computation.
- Psychology ,In-depth methods for automated reasoning,Automatic problem solvers and planners,Knowledge representation mechanisms,Game playing,Machine learning,Statistical pattern recognition, ,,Principles ,programming techniques,LISP,symbol manipulation
- knowledge representation,logical reasoning,probabilistic reasoning,learning,language understanding,vision,expert systems,social issues,planning,natural language understanding
- qualitative physicsmachine learning,formal models of time ,action,Principal ideas,developments in artificial intelligence,Problem solving,search,game playing,knowledge representation,reasoning,uncertainty,machine learning,natural language processing
- Nature and goals of AI,Searching state-spaces,states and transitions to model problems,Breadth-first ,depth-first and related types of search, A* search algorithm
- heuristics in search,Reasoning in logic,propositional and predicate logic,Different characterisations of reasoning,Generalized modus ponens
complex topics like:
- Resolution,Forward and backward chaining,Knowledge Representation,Diversity of knowledge,Inheritance hierarchies,Semantic networks
- Knowledge base ontologies,Handling uncertainty,Diversity of uncertainty,Inconsistency,Dempster-Shafer theory,Machine Learning,Induction of knowledge,Decision tree learning algorithms,Intelligent agents,architecture for intelligent agents,Argumentation
- Decision-making,Nature and Goals of Neural Computing,network architectures and learning paradigms,Binary Decision Neurons,McCullough-Pitts model
- Single-layer perceptrons and their limitations,Multilayer Perceptron,sigmoid output function,Hidden units and feature detectors,Training by error backpropagation
- error surface and local minima,Generalisation,overtraining,Hopfield Model,Content addressable memories and attractor nets,Hopfield energy function, Setting the weights,Storage capacity,Self-Organising Nets,Kohonen self-organising feature map
- Common Lisp,First-Class and Higher-Order Functions ,Anonymous Functions and Closures ,CLOS ,MACSYMA ,Constraint Satisfaction ,Natural Language Processing and Parsing
- Macros and Microlanguages ,Rule-Based Expert Systems and RETE ,Memoization ,Partial Evaluation ,Meta-Circular Evaluation ,Compiling LISP Programs
- STRIPs planning,Partial-order planning,Situation calculus,Theorem proving,GraphPlan,Transformational planning,Simulated annealing
- Motion planning,Case-based reasoning,Multi-agent coordination,Negotiation planning,Representation and Reasoning,Logical representation,Frame problem
- Probabilistic reasoning,Bayesian networks,Game Playing,Minimax search,Evaluation functions,Learning evaluation functions,Markov Decision Processes,Reinforcement learning for games,Developing AI agents,Multi-agent planning
- Methods & techniques with in the field of artificial intelligence, ,problem solving and optimisation ,representing and reasoning with uncertain knowledge and machine utility ,,of algorithms ,implementation in software
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