## Reinforcement learning in AI

Reinforcement learning Reinforcement learning is like many topics with names ending in -ing, such as machine learning, planning, and mountaineering, in that it is simultaneously a problem, a class of solution methods that work well on the class of problems, and the field that studies these problems and their solution methods. Reinforcement learning problems involve … Read more

## Utility theory in Artificial intelligence

There are following points about Utility theory Defines axioms on preferences that involve uncertainty and ways to manipulate them. Uncertainty is modeled through lotteries -– Lottery: [ p : A;(1 − p) : C] Outcome A with probability p Outcome C with probability (1-p) The following six constraints are known as the axioms of utility … Read more

## Probabilistic reasoning in Artificial intelligence

Need of Probabilistic Reasoning in AI Unpredictable outcomes Predicates are too large to handle Unknown error occurs In probabilistic reasoning, there are two methods to solve difficulties with uncertain knowledge Bayes’ rule Bayesian Statistics Probability: Probability can be defined as chance of occurrence of an uncertain event. It is the numerical measure of the likelihood … Read more

## Forward and Backward Chaining in Artificial intelligence

Forward chaining starts with the available data. This is an initial data and uses inference rules. It helps in extracting more data until a goal is reached. An inference engine using forward chaining searches the inference rules until it finds one. Here the antecedent is known to be true. Whenever such a rule is found, … Read more

## Inference in First order logic in Artificial intelligence

There are two ideas behind Inference in First order logic in Artificial intelligence convert the KB to propositional logic and use propositional inference a shortcut that manipulates on first-order sentences directly (resolution, will not be introduced here) Universal Instantiation infer any sentence by substituting a ground term (a term without variables) for the variable Examples … Read more

## Depth-first search in AI

Depth-first search always expands the deepest node in the current frontier of the search tree The search proceeds immediately to the deepest level of the search tree, where the nodes have no successors. As those nodes are expanded, they are dropped from the frontier, so then the search “backs up” to the next deepest node … Read more

## Uniform cost search in AI

uniform-cost search expands the node n with the lowest path cost g(n). This is done by storing the frontier as a priority queue ordered by g Uniform-cost search on a graph function UNIFORM-COST-SEARCH(problem) returns a solution, or failure node ←a node with STATE = problem.INITIAL-STATE, PATH-COST = 0 frontier ← a priority queue ordered by … Read more