Simplifying AI Models with the PEAS Representation System

NEHAAL PANDEY 13 Feb, 2024 • 7 min read

Introduction

Artificial Intelligence (AI) encompasses the remarkable ability of machines to exhibit rational thinking and human-like behavior. It serves as a dynamic subset within the broader field of data science, breathing life into machines. Within AI, data scientists analyze predictive data, leveraging algorithm-based machine learning—an integral artificial intelligence component.

Machines acquire knowledge through a variety of techniques and then put that knowledge to use when making decisions or carrying out tasks. With research to achieve level 5 automation for autonomous vehicles, AI is the future of self-driving cars. Based on the strength and application of AI, it can be broadly classified into three types. Weak, Strong, and Super AI.

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Learning Objectives

  1. Types of AI: Understand the distinctions between Weak AI, Strong AI, and Super AI, along with examples illustrating their applications.
  2. Composition of AI: Learn the fundamental components of an AI system, including agents and environments, and grasp the classifications of agents based on various features.
  3. Rational and Autonomous Agents: Comprehend the concepts of rational and autonomous agents in the context of AI and recognize their characteristics and functionalities.
  4. PEAS Framework: Gain insights into the PEAS (Performance, Environment, Actuators, Sensors) framework and its significance in designing intelligent systems.
  5. Sensors and Actuators: Explore the role of sensors and actuators in AI systems and understand how they contribute to an agent’s perception and interaction with the environment.
  6. Environment in AI: Learn about different types of environments in AI, such as fully observable vs. partially observable, static vs. dynamic, discrete vs. continuous, deterministic vs. stochastic, single-agent vs. multi-agent, and episodic vs. sequential.
  7. Performance Measure: Understand the importance of defining performance measures in AI and how they gauge an agent’s success or accuracy in achieving its goals.
  8. Practical Application: Apply the PEAS system concept to a real-world scenario, using the example of a ketchup-producing industry, to understand how it can be utilized for task optimization.
  9. Overall Significance of PEAS: Recognize the ongoing relevance of PEAS in AI research and engineering, particularly in constructing intelligent systems tailored to excel within specific domains.

This article was published as a part of the Data Science Blogathon.

Types of AI

  • Weak AI: Weak AI, or narrow AI, is the intelligence of a machine applied to a specific domain or application. It is the most common and widely present AI. Contrary to its name, Weak AI is a powerful, smart AI capable of thinking and acting rationally. When trained, It can perform one task perfectly. We can say that it has a very narrow approach to doing things. It will only achieve what it is trained or programmed to do. Examples include self-driving cars, Recommendation systems, spam filtering, fraud detection, etc. 
  • Strong AI: Strong AI(General AI) is believed to interact and operate various independent and unrelated tasks. It is said to have a human-like intelligence that can perform based on its thinking and decision-making strategies. Strong AI is still theoretical, as we only have weak AI now. (Yes, even the most competent AI are weak AI.) Adopting the AGI, i.e., Artificial General Intelligence, will take a long time. 
  • Super AI: Super AI, also known as conscious AI or artificial superintelligence (ASI), can surpass human intelligence. They are believed to have human-like consciousness. It can do anything in the best way. They are capable of making decisions rationally rather than humanely. Super AI is purely speculative at this stage and part of science fiction. Super AI can be thought of as ‘Robots taking over and enslaving humans. Super AI can be thought of as ‘Chitti’ from the movie Robot by the legendary Rajnikant.

Composition of AI

An AI system is composed of agents and environments.

Agents: Agents are software, hardware, or a combination of both devices operating in the environment to achieve certain goals. An Intelligent agent acts autonomously and persists over a longer period. It should also be able to adapt to changes in pursuit of a certain goal. Agents perceive through sensors and operate on the environment through actuators. An environment can have a single agent or multiple agents.

Agents can be classified based on various features:

  1. Simple Reflex agent
  2. Simple Reflex with state agent
  3. Goal-based agent
  4. Utility-based agent
  5. Learning-based agent

For example, voice assistants like Alexa and Siri are Softbots or software agents, and robotic cars are perfect examples of Hardware agents.

Rational Agent: An agent who has complete knowledge, clear preferences, models uncertainty, and behaves in a way to maximize its performance measure via all feasible actions is said to be acting rationally. A rational agent will always perform the right thing.

Autonomous Agent: An agent which can decide autonomously the actions that need to be taken in the current instance to maximize progress towards its goals.

You can also explore the tutorial: Introduction to Python

What is PEAS?

PEAS, crucial in AI design for tasks like web development, encapsulates performance, environment, actors, and sensors. PEAS serves as a framework for AI agents, addressing performance evaluation of the Environment, Sensors, and actuators.

It assesses an agent’s success in dynamic settings. Understanding the task environment aids in algorithmic optimization, making PEAS instrumental in crafting effective AI strategies for tasks, including web development.

Sensors: This criterion helps agents perceive their environment by giving them a complete set of Inputs. The action of agents depends on the history and the current input set. Examples of sensors include cameras, GPS, odometers, various sensing tools, etc.

Actuators: Actuators help agents operate in the environment. Actuators include display boards, object-picking arms, track-changing mechanisms, etc. Actions performed by agents can bring change to the environment as well.

Environment: The agent’s surroundings at a particular instant in which the agent works are called the environment. It can be static or dynamic based on the motion of the agent. A small change in the environment will also change the required sensors and actions of the Agent.

As per Russell and Norvig, an environment can be classified on various factors:

  1. Fully observable vs. Partially Observable
  2. Static vs. Dynamic
  3. Discrete vs. Continuous
  4. Deterministic vs. Stochastic
  5. Single-agent vs. Multi-agent
  6. Episodic vs. sequential

Performance measure: The performance measure defines an agent’s success or accuracy in achieving its set goals.

Also read: AI in Cybersecurity: What You Need to Know

Understanding PEAS with an Example

Let’s understand the PEAS system for a ketchup-producing industry. To produce good quality ketchup, it is important to segregate the ripe tomatoes and use them for production. Using bad-quality or unripe tomatoes can lower the quality of the ketchup. Now, classifying the tomatoes can be done manually, but it would be tedious for humans as a factory might process thousands of tomatoes daily. Hence, we can incorporate an AI to help us. Hence, we must know about our task environment and the agent we will use.

6 Great Sauce Tomatoes - FineGardening
(source: Fine Gardening)

Agent: Tomato classification system.

Sensors: Weighing sensors, Cameras for visual input, color sensing, etc.

Actuators: Track changing mechanism for segregation, display boards, or a Y-belt for quick classification into ripe and unripe tomatoes.

Environment: Our environment can be a moving walkway through which the tomatoes are passed on for segregation. It should have a good source of light for better camera input.

Performance: It measures how successful the agent is in classifying the tomatoes. It can be a confusion matrix with true positive, true negative, false positive, and false negative numbers or the model’s accuracy.

Conclusion

PEAS, which stands for Performance, Environment, Actuators, and Sensors, is a comprehensive framework for designing intelligent systems. It includes a speedometer, problem-solving, and natural language processing (NLP). PEAS descriptions emphasize crafting AI models adept at real-world scenarios by capturing the dynamic interplay of these key elements. Moreover, PEAS, a crucial performance metric, will continue to be crucial for AI researchers and engineers, offering guidance in constructing intelligent systems that excel within their specific domains.

Key Takeaways

  • We learned about the 3 types of AI.
  • We understood the concept of Agents and the environment.
  • We learned about the PEAS system and its importance.
  • Finally, we saw an example of defining the PEAS system.

Frequently Asked Questions

Q1. What are sensors in AI?

Ans. Sensors in AI are devices or components that gather data from the environment or the system they are integrated with. They capture various types of information, such as temperature, light, sound, motion, or chemical composition. AI algorithms then utilize these data inputs to make informed decisions, learn patterns, or intelligently interact with the environment.

Q2. What are the different types of agents?

Ans. Different types of agents in AI include:
A. Simple Reflex Agents: React to the current state of the environment without considering history.
B. Model-Based Reflex Agents: Utilize internal models to make decisions based on the current state and history.
C. Goal-Based Agents: Take actions based on achieving specific goals.
D. Utility-Based Agents: Consider both goals and utility to maximize overall satisfaction.
E. Learning Agents: Learn from their experiences and improve their performance over time.

Q3.What are PEAS for a self-driving car agent?

Ans. PEAS for a self-driving car agent: Performance (efficient navigation and collision avoidance), Environment (road conditions and traffic), Actuators (steering, acceleration, braking), and Sensors (cameras, lidar, radar). The agent’s goal is safe and effective autonomous driving, balancing speed, precision, and adherence to traffic rules for optimal performance.

Q4. What is AutoML in Machine Learning?

Ans. AutoML, or Automated Machine Learning, refers to using automated tools and algorithms to streamline and automate the process of building, training, and deploying machine learning models. It aims to make machine learning accessible to non-experts by automating various tasks like feature engineering, model selection, and hyperparameter tuning.

Q5. How does the PEAS framework apply to a chatbot system?

Ans. The PEAS (Performance measure, Environment, Actuators, Sensors) framework guides the design of chatbot systems. It defines how the system performs (Performance measure), operates in its context (Environment), executes actions (Actuators), and perceives user inputs (Sensors). It ensures effective, goal-oriented functionality, enhancing the chatbot’s overall performance.

Q6. What are the components of PEAS in an AI chess-playing agent?

Ans. PEAS (Performance measure, Environment, Actuators, Sensors) components for an AI chess-playing agent include:
A. Performance Measure: Winning the game or achieving checkmate.
B. Environment: Chessboard, pieces, and rules.
C. Actuators: Move generation and piece placement.
D. Sensors: Board state perception and opponent’s moves.

Q7. What is PEAS in AI for vacuum cleaners?

Ans. PEAS (Performance measure, Environment, Actuators, Sensors) for a vacuum cleaner in AI would involve measuring cleaning efficiency (performance), the home environment with obstacles (environment), motors and brushes for movement (actuators), and sensors for detecting dirt and navigation (sensors), collectively guiding the vacuum’s intelligent operation.

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NEHAAL PANDEY 13 Feb 2024

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