Structure of Agents

Table based agent

In table based agent the action is looked up from a table based on information about the

agent’s percepts. A table is simple way to specify a mapping from percepts to actions. The

mapping is implicitly defined by a program. The mapping may be implemented by a rule

based system, by a neural network or by a procedure.

There are several disadvantages to a table based system. The tables may become very large.  Learning a table may take a very long time, especially if the table is large. Such systems

usually have little autonomy, as all actions are pre-determined.

Percept based agent or reflex agent

In percept based agents,

1. information comes from sensors - percepts

2. changes the agents current state of the world

3. triggers actions through the effectors

Such agents are called reactive agents or stimulus-response agents. Reactive agents have no

notion of history. The current state is as the sensors see it right now. The action is based

on the current percepts only.

The following are some of the characteristics of percept-based agents.

• Efficient

• No internal representation for reasoning, inference.

• No strategic planning, learning.

• Percept-based agents are not good for multiple, opposing, goals.

State-based Agent or model-based reflex agent

State based agents differ from percept based agents in that such agents maintain some sort of state based on the percept sequence received so far. The state is updated regularly based

on what the agent senses, and the agent’s actions. Keeping track of the state requires that

the agent has knowledge about how the world evolves, and how the agent’s actions affect

the world.

Thus a state based agent works as follows:

• information comes from sensors - percepts

• based on this, the agent changes the current state of the world

• based on state of the world and knowledge (memory), it triggers actions through

the effectors

Goal-based Agent

The goal based agent has some goal which forms a basis of its actions.

Such agents work as follows:

• information comes from sensors - percepts

• changes the agents current state of the world

• based on state of the world and knowledge (memory) and goals/intentions, it

chooses actions and does them through the effectors.

Goal formulation based on the current situation is a way of solving many problems and

search is a universal problem solving mechanism in AI. The sequence of steps required to

solve a problem is not known a priori and must be determined by a systematic exploration

of the alternatives.

Utility-based Agent

Utility based agents provides a more general agent framework. In case that the agent has

multiple goals, this framework can accommodate different preferences for the different

goals.

Such systems are characterized by a utility function that maps a state or a sequence of

states to a real valued utility. The agent acts so as to maximize expected utility.

Learning Agent

Learning allows an agent to operate in initially unknown environments. The learning

element modifies the performance element. Learning is required for true autonomy.

Conclusion

In conclusion AI is a truly fascinating field. It deals with exciting but hard problems. A

goal of AI is to build intelligent agents that act so as to optimize performance.

• An agent perceives and acts in an environment, has an architecture, and is implemented

by an agent program.

• An ideal agent always chooses the action which maximizes its expected performance,

given its percept sequence so far.

• An autonomous agent uses its own experience rather than built-in knowledge of the

environment by the designer.

• An agent program maps from percept to action and updates its internal state.

• Reflex agents respond immediately to percepts.

• Goal-based agents act in order to achieve their goal(s).

• Utility-based agents maximize their own utility function.

• Representing knowledge is important for successful agent design.

• The most challenging environments are partially observable, stochastic, sequential,

dynamic, and continuous, and contain multiple intelligent agents.

Presence of Other agents

Single agent/ Multi-agent

A multi-agent environment has other agents. If the environment contains other intelligent

agents, the agent needs to be concerned about strategic, game-theoretic aspects of the

environment (for either cooperative or competitive agents)

Most engineering environments do not have multi-agent properties, whereas most social

and economic systems get their complexity from the interactions of (more or less) rational