## Schedule
## Introduction to A. I.Intelligent agent
Terminology- Fully vs. Partially Observable
*visibility (chess vs. poker)* - Deterministic
*(outcome consistent with action, e.g. chess)*vs. Stochastic*(random factor, e.g. dice)* - Discrete vs. Continuous
*(whether finite/infinite possibilities spacewise, e.g. chess vs. dart)* - Benign vs. Adversarial
*motivation to bother or counteractiveness*
Sources of uncertainty: Stochastic environments, sensor limits, adversaries, laziness, ignorance
## Problem SolvingComparison of frontier and explored set Breadth First Search: scans all possible paths at the same step Depth First Search: scans one particular full path at a time Uniform Cost Search: propagates with the lowest distance first
A* Search: proceeds with lowest (distance to destination(h) + distance travelled) on condition that h < true cost
(admissible)
State Spaces: product of multi-dimensional conditions
- admissible: describing a heuristic that never overestimates the cost of reaching a goal
- guaranteed to work when fully observable, known, deterministic, discrete and static
## Statistics Uncertainty, and Bayes networksBayes Rule: P(A|B) = P(B|A) * P(A)/P(B) Complex Bayes network: multiply all conditional probabilities, and analyze the provided distribution D-Separation/Reachability
Conditional independence: disjoint relationship, linked by known cause, linked by unknown effect Conditional dependence: direct causal relationship, linked by unknown cause, linked by known effect, or its successor
Minimum number of parameters necessary to specify joint probability = ∑ 2^(number of causes for each nodes)
## Machine LearningMaximum likelihood: proportionate ratio
Laplace smoothing: P = (occurrence + k) / (data + #variables)
Linear Regression: minimize the sum of errors between real value-calculated value
Perceptron Algorithm: linear separation by taking the majority class label of k(recolarizer) nearest neighbors Unsupervised Learning
## Hidden Markov models and Bayes filtersPropositional Logic
## Markov Decision Processes and Reinforcement Learning## Adversarial planning (games) and belief space planning (POMDPs)Logic and Logical Problem Solving
Robotics and robot motion planning Natural Language Processing and Information Retrieval |

Sciences >