Artificial Intelligence and Machine Learning: Unit I(b): Intelligent Agents and Problem Solving Agents

Designing an Agent System

Intelligent Agents and Problem Solving Agents - Artificial Intelligence and Machine Learning

When we are specifying agents we need to specify performance measure, the environment and the agent's sensors and actuators. We group all these under the heading of the task environment.

Designing an Agent System

When we are specifying agents we need to specify performance measure, the environment and the agent's sensors and actuators. We group all these under the heading of the task environment.

For the acronymically we call this PEAS ([P]erformance, [E]nvironment, [A]ctuators, [S]ensors) description.

Steps in Designing an Agent

1) Define problem area (i.e. task environment) in complete manner. Example-Vaccum world, automated face recognition, automated taxi driver.

2) Define or tabulate PEAS.

3) Define or tabulate agent functions (i.e. percept sequence and action column)

4) Design agent program.

5) Design an architecture to implement agent program.

6) Implement an agent program.

The agent system may be single agent or multiple agents system.

If system is multiagents then we need to consider communication, co-operation strategies among multiple agents.

Examples of Agent Types and their PEAS Description According to their Uses

1) General Purpose (uses for common man)

II) Industrial Business Purpose:

III) Scientific Research Purpose

IV) Medical Purpose

V) Educational Purpose

The Detail Example of PEAS

Agent Interactive English Tutor

1) The [P]erformance Measures:

The Interactive English Tutor agent system must achieve the following performance measures.

1)  All the student must get maximum knowledge regarding English subject, such as vocabulary, verbal soft skills, (i.e. communicational skill), reading, writing skills.

2) All the students must score good marks in the English test.

II) The [E]nvironment:

In Interactive English Tutor agent system environment has following properties:-

1) All the students having different grasping power and IQ (Intellectual Quotient).

2) Software modules which gives demonstration.

III) The [A]ctuators (Actions):

The software model (agent program) will be executed on the agent architecture. (i.e. operating system). The actions performed by interactive English tutor are,

1) Audio / video demonstration on different topics.

2) Practical assignment on verbal written skills, report generation, letter writing, etc.

3) Monitoring and inspection (i.e. checking) of the practical assignment provided with suggestions and corrections, to students.

4) Online test conduction and result analysis.

5) Student's speech and video recording.

IV) The [S]ensors:

Sensor plays a crucial role in interactive English tutor agent system. The following sensor are required to support sequence of perception:-

1) Keyboard for providing input events.

2) Mouse for GUI interface.

3) Headphone for listening and mike for audio recording.

4) Video/web camera's for video shooting.

Artificial Intelligence and Machine Learning: Unit I(b): Intelligent Agents and Problem Solving Agents : Tag: : Intelligent Agents and Problem Solving Agents - Artificial Intelligence and Machine Learning - Designing an Agent System


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Artificial Intelligence and Machine Learning

CS3491 4th Semester CSE/ECE Dept | 2021 Regulation | 4th Semester CSE/ECE Dept 2021 Regulation