Skip to document

AISC subj Exp1 - Identification of the problem, Defining PEAS Description and Problem formulation.

Task environments, which are essentially the "problems" to which ratio...
Course

Computer Science, Engineering (CSC502)

41 Documents
Students shared 41 documents in this course
Academic year: 2021/2022
Uploaded by:

Comments

Please sign in or register to post comments.

Preview text

Experiment 1

Aim: Identification of the problem, Defining PEAS Description and Problem

formulation.

S/W Used: Case Study

Theory:

1 Specifying Task Environment

Task environments, which are essentially the "problems" to which rational agents are the "solutions.” In designing an agent, the first step must always be to specify the task environment as fully as possible with the help of PEAS (Performance, Environment, Actuators, Sensors) description.

With educational institutes closed due to the COVID-19 pandemic, the government has been encouraging online education to achieve academic continuity. Most high-end private and public institutions have made the switch smoothly using online platforms such as Zoom, Google classrooms, Microsoft teams, etc.

1 PEAS

PEAS stands for “Performance Environment Actuator Sensor”. It is used to specify the setting for an intelligent agent design. PEAS is a type of model on which an AI agent works upon. An Interactive Tutor will make sure to identify problem areas, prepare strategies, schedule periodic doubt solving session so the students can achieve the best results.

Agent: Refinery Controller

P = Maximum Purity, yield, safety E = Refinery, operators A= Valves, Pumps, Heaters, Displays S = Temperature, Pressure, Chemical sensors

1 Problem Formulation

Problem formulation means choosing a relevant set of states to consider, and a feasible set of operators for moving from one state to another. Search is the process of considering various possible sequences of operators applied to the initial state, and finding out a sequence which culminates in a goal state.

An initial state is the description of the starting configuration of the agent An action or an operator takes the agent from one state to another state which is called a successor state. A state can have a number of successor states. A plan is a sequence of actions. The cost of a plan is referred to as the path cost. The path cost is positive.

Initial State: Seperation

Goal: Transforms crude oil into useful gases such as LPG,

gasoline, jet fuel, etc.

Successor: 1 2 3. reshape 4 5

Path Cost: 5

Conclusion:

Thus we have understood the concept of PEAS description and problem formulation and have implemented it by identifying a problem and writing a PEAS description and problem formulation for the same

Was this document helpful?

AISC subj Exp1 - Identification of the problem, Defining PEAS Description and Problem formulation.

Course: Computer Science, Engineering (CSC502)

41 Documents
Students shared 41 documents in this course
Was this document helpful?
Experiment 1
Aim: Identification of the problem, Defining PEAS Description and Problem
formulation.
S/W Used: Case Study
Theory:
1.1
Specifying Task Environment
Task environments, which are essentially the "problems" to which rational agents are the
"solutions.” In designing an agent, the first step must always be to specify the task
environment as fully as possible with the help of PEAS (Performance, Environment,
Actuators, Sensors) description.
With educational institutes closed due to the COVID-19 pandemic, the government has
been encouraging online education to achieve academic continuity. Most high-end
private and public institutions have made the switch smoothly using online platforms
such as Zoom, Google classrooms, Microsoft teams, etc.
1.2
PEAS
PEAS stands for “Performance Environment Actuator Sensor”. It is used to specify the
setting for an intelligent agent design. PEAS is a type of model on which an AI agent
works upon. An Interactive Tutor will make sure to identify problem areas, prepare
strategies, schedule periodic doubt solving session so the students can achieve the best
results.
Agent: Refinery Controller
P = Maximum Purity, yield, safety
E = Refinery, operators
A= Valves, Pumps, Heaters, Displays
S = Temperature, Pressure, Chemical sensors