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### 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 Solving

Comparison 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 networks

Bayes 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 Learning

Maximum 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 filters

Propositional Logic

### Adversarial planning (games) and belief space planning (POMDPs)

Logic and Logical Problem Solving

Image Processing and Computer Vision

Robotics and robot motion planning

Natural Language Processing and Information Retrieval