Artificial Intelligence: A Modern Approach Chapter 2 Intelligent Agents

Anything that can be view as perceiving its environment through sensors and acting upon that environment through actuators.

An agent's perceptual inputs at any given instant

The complete history of everything the agent has ever perceived.

A mapping of a given percept sequence to an action.

The internal implementation of an agent's agent function.

The evaluation of the desirability of any given sequence of environment states.

The performance measure that defines the criterion of success.The agent's prior knowledge of the environment.The actions that the agent can perform.The agent's percept sequence to date.For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge the agent has.

Doing actions in order to modify future percepts.

As the agent gains experience, its initial configuration could be modified or augmented

The ability to learn how to compensate for partial or incorrect prior knowledge

The problems to which rational agents are the solution. PEAS (Performance Measures, Environment, Actuators, Sensors).

The external forces or actors acting on, changing, or causing a problem.

Control of actions, production of results

Receiving input from all sources necessary to solve the problem.

If an agent's sensors give it access to the complete state of the environment as each point in time, then we say that the task environment is fully observable. (Fully/Partially)

Number of agents in an environment as well as the manner in which they interact (competitive, cooperative).

If the next state of the environment is completely determined by the current state and the action executed by the agent, then is is deterministic.

An environment in which actions are characterized by their possible outcomes, but no probabilities are attached to them.

A partially observable or stochastic environment.

Experience can be divided into atomic episodes.

In each episode the agent receives a percept and performs an action. The next episode does not depend on the previous one.

If the environment can change while the agent is deliberating then it is dynamic.

The environment does not change but an agent's performance does.

If an agent's action and percepts and the state can have an infinite number of values at any given time then the environment is continuous.

In a known environment, the outcomes for all actions are given.

Selects actions on the basis of the current percpt, ignoring the rest of the percept history.

An agent that keeps track of unobservable aspects of the current state using the percpet to develop a model.

An agent that combines a world model with a goal that describes desirable states to make decisions.

The measure of desirability

An internalization of an agent's performance measure

The expected value of an action's outcome in a partially unobservable or stochastic environment.

An agent that can compensate for partial or incorrect knowledge.

Responsible for making improvements

Responsible for selecting external actions, it takes in percpets and decides on actions.

Responsible for determining the how the agent is doing and modifying the performance element.

It is responsible for suggesting actions that will lead to new and informative experiences.

Each state of the world is indivisible, it has no internal structure.

Splits up each state into a fixed set of variable or attributes, each of which can have a value.

each state consists of objects which may have attributes of the their own and relationships to other objects

Complexity of learning and reasoning increases with expressiveness. The Conciseness of states increases with expressiveness. More expressive languages can capture at least as much information as less expressive ones but more concisely and complexly.