10/19/2018
10/16/2018
Search Algorithms
Types of Search
Uninformed Search
It uses no domain knowledge. The basic strategies include- Breadth-first search (BFS): expand shallowest node first (node having the lowest depth)
- Depth-first search (DFS): expand deepest node first
- Depth-limited search (DLS): depth first with increasing level
- Iterative-deepening search (IDS): depth limited with increasing limit
- Uniform-cost search (UCS): expand least cost node
Informed Search
It uses domain knowledge. The basic strategies include- Best first search (Greedy search): expand the node that appears to be closet to goal
- A* search: minimize the total estimated solution cost
10/13/2018
Intelligent agent (IA)
What is Intelligent Agent (IA)?
Agent = Architecture + Program
Architecture = infrastructure to host the agent program, e.g. physical sensors, computing devices
Program = the logic and algorithms
In general, IA contains the sensors to precept the environment, think how to do, and then act through the actuators.
[Wikipedia] In artificial intelligence, an intelligent agent (IA) is an autonomous entity which observes through sensors and acts upon an environment using actuators (i.e. it is an agent) and directs its activity towards achieving goals (i.e. it is "rational", as defined in economics[1]). Intelligent agents may also learn or use knowledge to achieve their goals. They may be very simple or very complex. A reflex machine, such as a thermostat, is considered an example of an intelligent agent.
Type of IA
- simple reflex agents
- model-based reflex agents
- goal-based agents
- utility-based agents
- learning agents
Intelligence Level
Simple Reflex Agents
Environment: fully observableAgent Function: based on condition-action rule
Model-based Reflex Agents
Environment: partially observablePrecepts -> State -> Internal Model -> Action
Goal-based Agents
Environment: partially observablePrecepts -> State (Goal or Non-Goal) -> Internal Model -> Action
Utility-based Agents
Environment: partially observableSimilar to goal-based, and additionally use utility function to determine which actions is better
Learning Agents
Environment: can be unknown
Learning from its experiences and knowledge to achieve their goal.
Learning from its experiences and knowledge to achieve their goal.
10/03/2018
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Python For Data Science - Jupyter Notebook
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What is Intelligent Agent (IA)? Agent = Architecture + Program Architecture = infrastructure to host the agent program, e.g. physic...
