A.I. Chapter 2 rational agents

Multi-agent

B's behavior maximizes a performance measure whose value depends on agent A's behavior. Communication emerges as rational behavior.

Model

Based on information about how the world evolves independently of the agent

Goal-based agent

Can combine some sort of goal information of desirable situations and the model to choose actions to achieve the goal. Act to achieve their goals.

Sequential

Current decision could affect all future decisions

Information Gathering

Doing actions in order to modify future percepts

Partially Observable

Due to noisy and inaccurate sensors or because parts of the state are simple missing from the sensor data

Rational agent

For each possible percept sequence, it 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 it has

Unobservable

If an agent has no sensors at all

Fully Observable

If an agent's sensors give it access to the complete state of the environment at each point in time (relevant to the choice of action, depending on performance measure)

Autonomy

An agent should learn what it can to compensate for partial or incorrect prior knowledge

Non-deterministic

An environment in which actions are characterized by their possible outcomes but no probabilities are attached to them Usually associated with performance measures that require the agent to succeed for all possible outcomes of its actions.

Utility function

An internalization of the performance measure.

True

Any rational agent must behave as if it posses a utility function whose expected value it tries to maximize.

Rationality

1. The performance measure that defines the criterion of success. 2. The agent's prior knowledge of the environment 3. The actions that the agent can perform 4. The agent percept sequence to date

Uncertain

If an environment is not fully observable or deterministic

Dynamic

If the environment can change while the agent is deliberating

Static

If the environment does not change while the agent is deliberating

Discrete

If the environment has a finite set of states

Deterministic

If the next state of the environment is completely determined by the current state and action executed by the agent

Simple reflex agent property

If will work only if the correct decision can be made on the basis of only the current percept - that is, only if the environment is fully observable

Agent Program

Implements agent function

Randomization

In a single-agent environment, _________ is usually not rational.

Critic

Provides feedback to learning element on how the agent is doing and determines how the performance element should be modified to do better in the future.

Learning element

Responsible for making improvements

Performance element

Responsible for selecting external action

Task Environment

The "problems" to which rational agents are the "solutions"

Model-based reflex agent

The agent maintains internal state that depends on the percept history and thereby reflects at least some of the unobserved aspects of the current states Maintain internal state to track aspects of the worlds that are not evident in the current state.

Unknown

The agent will have to learn how it works in order to make good decisions. Could be fully observable

Episodic

The agent's experience is divided into atomic episodes, and in each, the agent receives a percept and then performs a single action.

Known

The outcome for all possible states should be given. Could be partially observable

Continuous

There is a range of states that it could go through

Simple reflex agent

These agents select the actions on the basis of the current percept, ignoring the rest of the percept history

In some competitive environments, randomized behavior is rational because it avoids the pitfalls of predictability.

True

Utility-based agent

try to maximize their own expected "happiness"