- Search
- Goal State <> Current State
- Informed Search
- Choosing next state based on distance from goal state.
- Greedy Best First Search
- Distance of next state from goal state
- A* Search
- Distance to next state + Distance of next state from goal state
- Optimization
- Searching for maximum / minimum.
- Modifying the current state and evaluating to see if it’s comparatively optimum than the current state.
Algorithms
Hill Climbing
Simulated Annealing
- Local Beam Search
- Keeping “a number of” “current states” in memory
- Algorithms
Genetic Algorithm
- Constraint Satisfaction Search
- Search + Constraint Propagation
- Planning
- Search utilizing concepts from logic.
- logical AND
- => decomposition of problem
- Uncertain / Probabilistic Reasoning
- Probabilistic Reasoning over time
- Models
- Hidden Markov Model
- Dynamic Bayesian Network
- Simple Decision Problems
- Decision Theory = Probability Theory + Utility Theory
- Complex Decision Problems
- Game Theory
- Mechanism Design
- Reinforcement Learning
- Inductive / Statistical Learning: Learning input/output pair for a particular problem.
- Knowledge based learning: Adding to what you already know as you go along learning new things.
- Reinforcement Learning: Learning sequence of behaviors from feedback.
- Communicating with real world
- Natural Language Processing
- Parsing, Semantics, etc.
- Statistical Language Processing
- Probabilistic Language Models
- Counting occurrence of words, N-gram models.
- Information Retrieval, Information Extraction, Machine Translation.
- Perception
- Computer Vision
- Robotics
- Sensors
- Localization
- Mapping
- Actuators
- Degrees of freedom
- Software Architecture
- Reactive Architecture
- Probabilistic Robotics [3]
References
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