Different Types of Agents

Different Types of Agents

Intelligent Agent

"Intelligent agent is an intelligent actor, who observe and act upon an environment".

Intelligent agent is magnum-opus.

The term 'Intelligent thinker' is different from intelligent agent. Fig. 2.11.1 shows intelligent agent's behaviour.

Example:

1) A robotic agent (Cameras, Infrared range finders).

2) An embedded real time software system agent.

3) A human agent (Eyes, ears and other organ).

Characteristics of Intelligent Agent (IA)

1) The IA must learn and improve through interaction with the agent environment.

2) The IA must adapt online and in the real time situation.

3) The IA must learn quickly from large amounts of data.

4) The IA must accommodate new problem solving rules incremently.

5) The IA must have memory which must exhibit storage and retrival capacities.

6) The IA should be able to analyze self in terms of behaviour, error and success.

Different Forms of Agents: (Types of Agents)

In artificial intelligence, there are different forms of intelligent agent and sub-agents.

As the degree of perceived intelligence and capability varies, it is possible to frame agent's into four categories.

1. Simple reflex agents.

2. Model based reflex agents.

3. Goal based agents.

4. Utility based agents.

In the following section we discuss each type of agent in detail.

1. Agent Type 1

Simple Reflex Agent

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

Property:

1) These are very simple but their intelligence is limited.

2) They will work only if correct decision can be made on the basis of only the current percept- that is only if the environment is fully observable.

3) A little bit of unobservability can cause serious trouble.

4) If simple reflex agent works in partially observable environment then, it can lead to infinite loops.

5) Infinite loops can be avoided if simplex reflex agent can try out possible actions i.e can randomize the actions.

6) A randomize simple reflex agent will perform better than deterministic reflex agent.

Example:

In ATM agent system if PIN matches with given account number then customer getsmoney.

Procedure: SIMPLE - REFLEX - AGENT

Input: Percept

Output: An action.

Static: Rules, a set of condition - action rules.

1. State← INTERPRET - INPUT (percept)

2. rule← RULE - MATCH (state, rules)

3. action← RULE - ACTION (rule)

4. return action.

2. Agent Type 2

Model Based Reflex Agent

Internal state of the agent stores current state of environment which describes part of unseen world i.e how world evolves, and effect of agent's own actions. It means that it stores model of possibilities around it. Hence it is called as model based reflex agent.

Property:

1) It has ability to handle partially observable environments. 2) Its internal state is updated continuously which can be shown as:

Old - Internal state + Current percept = Update state.

For example:

A car driving agent which maintains its own internal state and then take action as environment appears to it.

Procedure: REFLEX-AGENT-WITH-STATE

Input: Percept.

Output: An action.

Static State, a description of the current world state, rules, a set of condition- action rules, action, the most recent action, initially none.

1. State← UPDATE-STATE (state, action, percept)

2. Rule← RULE-MATCH (state, rules)

3. Action←RULE-ACTION (rule)

4. return action.

3. Agent Type 3

Goal based agent stores state description as well as it stores goal states information.

Property

1) Goal based agent works simply towards achieving goal.

2) For tricky goals it needs searching and planning.

3) They are dynamic in nature because the information description appears in proper and explicit manner.

4) We can quickly change goal based agent's behaviour for new/unknown goal.

For example:

Agent searching a solution for 8-queen puzzle.

4. Agent Type 4

Utility Based Agent

In complex environment only goals are not enough for agent designs. Additional to this we can have utility function.

Property:

1) Utility function maps a state on to a real number, which describes the associated degree of best performance.

2) Goals gives us only two outcomes achieved or not achieved. But utility based agents provide a way in which the likelihood of success can be measured against importance of the goals.

3) Rational agent which is utility based can maximize expected value of utility function i.e more perfection can be achieved.

4) Goals gives only two discrete states,

a) Happy b) Unhappy.

For example-

Millitary planning robot which provides certain plan of action to be taken. Its environment is too complex, and expected performance is also high.

Learning Agent

If agent is to operate initially in unknown environments then agent should be self-learner. It should observe and gain and store information. Learning agent can be divided into 4 conceptual components.

1) Learning Element - Which is responsible for making improvements.

2) Performance Elements - Which is responsible for selecting external actions.

3) Critic - It tells how agent is doing and determines how the performance element should be modified to do better in the future.

4) Problem Generator - It is responsible for suggesting actions that will lead to new and informative experiences to agent. Agent can ask problem generator for suggestions.

The performance standards distinguishes part of the incoming percept as a reward (success) or penalty (failure) that provides direct feedback on the quality of the agent's behaviour.

All four types agent we have seen can improve their performance through learning and there by become learning agents.

For example:

Aeroplane driving agent which continuously learns from environment and then do safe plane driving.

1. Components of Learning Agent

1) Base/Learner/Learning element - It holds basic knowledge and learn new things from the unfamiliar environment.

2) Capable/Efficient system/Performing elements - Capable system is responsible for selecting external actions. Performance element is the actual agent. It perceives and decides actions.

3) Fault reflector element - It gives feedback. It reflects fault and analyze corrective actions in order to get maximum success.

4) New problem generator element - It generate new and informative experience. It suggests new actions.

The performance standard makes difference between incoming percept as a reward (or penalty), that indicate direct feedback on the quality of the agent's behaviour.

More Types of Agents

We can do classification of agents based on various aspects like –

• Task they perform.

• Their various control architecture.

• Depending on sensitivity of their sensors, and effectiveness of their action and internal states they possess.

Following are various types of agents, based on above classification criteria :-

1. Physical Agents: A physical agent is an entity which perceives through sensors and acts through actuators.

2. Temporal Agents - A temporal agent may use time based stored information to offer instructions or data acts to a computer program or human being and takes program inputs percepts to adjust its next behaviour.

3. Spatial Agents - That relate to the physical real-world.

4. Processing Agents - That solve a problem like speech recognition.

5. Input Agents - That process and make sense of sensor inputs- e.g. neural network based agents.

6. Decision Agents - That are geared upto do decision making.

7. Believable Agents - An agent exhibiting a personality via the use of an artificial character (the agent is embedded) for the interaction.

8. Computational Agents: That can do some complex, lengthy scientific computations as per problem requirements.

9. Information Gathering Agents - Who can collect (perceive) and store data.

10. Entertaining Agents - Who can perform something which can entertain human like gaming agents.

11. Biological Agents - Their reasoning engine works almost identical to human brain.

12. World Agents - That incorporate a combination of all the other classes of agents to allow autonomous behaviours.

13. Life Like Agents - Which are combinations of other classes of agents which will behave like real world characters. (For example - A robotic dog)