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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