Building Expert Systems with PyKE

Building Expert Systems with PyKE

Table of Contents:

1. Introduction
2. Building an Expert System
   1. Components of an Expert System
   2. Types of Expert Systems
   3. Advantages and Disadvantages of Expert Systems
3. Development Roles in Building an Expert System
   1. Domain Expert
   2. Knowledge Engineer
   3. Programmer
   4. Project Manager
   5. End User
4. Knowledge Acquisition and Knowledge Engineering
   1. Knowledge Acquisition Process
   2. Knowledge Engineering Process
   3. The Role of Pike in Expert System Development
5. Example: Family Relations Expert System
   1. Fact Bases and Rule Bases
   2. Forward Chaining Rules and Backwards Chaining Rules
6. Example: Weather Expert System
   1. Fact Bases and Rule Bases
   2. Backwards Chaining Rules
   3. Question Bases
7. Conclusion
8. FAQ

Introduction

Welcome to lecture 13 of "Foundations of Artificial Intelligence". In this lecture, we will be learning about building an expert system and a programming shell called Pike. In our last lecture, we covered the major components of an expert system and discussed the advantages and disadvantages of using expert systems.

Building an Expert System

Before we dive into building an expert system, let's briefly review the components of an expert system. An expert system consists of various components, including a knowledge base, an inference engine, a user interface, a knowledge base editor, and an explanation system. These components work together to store and process knowledge, make decisions, Interact with users, and provide explanations.

There are different types of expert systems, including fuzzy expert systems, frame-Based expert systems, and neuro-fuzzy expert systems. Each Type has its own strengths and weaknesses and is suited for different applications.

Expert systems offer many advantages, such as capturing and disseminating expertise, providing consistent and reliable advice, and offering expertise that is not affected by human factors. However, they also have limitations, including the cost and time required for development and the need for available and cooperative experts.

Next, let's talk about the development roles involved in building an expert system. These roles include the domain expert, the knowledge engineer, the programmer, the project manager, and the end user. The success of developing an expert system depends on how well these roles work together and collaborate.

Development Roles in Building an Expert System

The development of an expert system requires a team of individuals with different roles and expertise.

• The domain expert is a subject matter expert who possesses deep knowledge and expertise in a specific domain. They work closely with the knowledge engineer to provide the necessary knowledge for the expert system.

• The knowledge engineer is responsible for designing, building, and testing the expert system. They work with the domain expert to extract knowledge and organize it into a structured representation.

• The programmer is responsible for coding the knowledge into the expert system using a programming language such as Python. They work closely with the knowledge engineer to implement the knowledge base and inference engine.

• The project manager is the leader of the expert system development team. They are responsible for keeping the project on track and ensuring that all milestones and deliverables are met. They also interact with all other members of the team.

• The end user is the person who uses the expert system once it is developed. They provide feedback on the usability and effectiveness of the system.

Knowledge Acquisition and Knowledge Engineering

Knowledge acquisition is the process of extracting Relevant knowledge from various sources, including human experts and existing databases or documents. It involves eliciting knowledge from the domain expert, organizing it into a structured format, and representing it in a form that can be processed by the expert system.

Knowledge engineering is the process of building the expert system's knowledge base based on the acquired knowledge. It involves choosing the appropriate representation and modeling techniques, designing the structure of the knowledge base, and implementing the rules and facts that make up the knowledge base.

Pike is a Python-based expert system programming shell that integrates logic programming into Python. It provides an inference engine and a coding framework for building expert systems. With Pike, You can perform forward chaining and backward chaining, and it allows for the construction of customized call graphs using Python functions.

Example: Family Relations Expert System

In this example, we will explore a family relations expert system built using Pike. The expert system uses forward chaining rules and stores facts in a knowledge fact base. By inputting information about family relationships, the system can infer additional relationships based on the given facts. The expert system utilizes rule bases and facts to determine relationships such as father-son, son-mother, and so on.

Example: Weather Expert System

Another example is a weather expert system that uses backwards chaining rules. The system determines what the user should bring when going outside based on weather conditions. It asks questions about rainfall, wind, and any ongoing disasters to make a recommendation. The system utilizes question bases and rule bases to provide tailored suggestions.

Conclusion

In conclusion, building an expert system involves various development roles, knowledge acquisition, and knowledge engineering. Pike is a powerful tool for creating expert systems in Python, allowing for both forward and backward chaining. Through examples of family relations and weather expert systems, we have demonstrated the practical application and capabilities of expert systems.

FAQ

Q: What are the major components of an expert system? A: The major components of an expert system include the knowledge base, inference engine, user interface, knowledge base editor, and explanation system.

Q: What are the advantages of using expert systems? A: Expert systems offer advantages such as capturing and disseminating expertise, providing consistent advice, and functioning without human factors.

Q: What are the limitations of expert systems? A: Limitations of expert systems include the cost and time required for development, the need for available and cooperative experts, and potential difficulties in capturing tacit knowledge.

Q: What roles are involved in developing an expert system? A: The roles involved in developing an expert system include the domain expert, knowledge engineer, programmer, project manager, and end user.

Q: What is the process of knowledge acquisition in expert system development? A: Knowledge acquisition involves extracting relevant knowledge from various sources, such as human experts or existing databases, and organizing it into a structured format for the expert system.

Q: What is Pike and how does it aid in expert system development? A: Pike is a Python-based expert system programming shell that integrates logic programming into Python. It provides an inference engine and coding framework for building expert systems.

Q: How do forward chaining and backward chaining differ in expert system development? A: Forward chaining involves starting with initial facts and deriving new facts using rules, while backward chaining starts with the desired goal and uses rules to prove or derive the required facts.

Q: Can Pike be used for both forward and backward chaining? A: Yes, Pike supports both forward and backward chaining, allowing for the development of expert systems that utilize either or both methods.

Q: What is the AdVantage of using backward chaining in expert systems? A: Backward chaining allows for a more goal-oriented approach, starting with the desired outcome and working backward to prove or derive the required facts.

Q: Can Pike handle real-time updates and modifications in the knowledge base? A: Yes, Pike allows for real-time updates and modifications in the knowledge base, making it suitable for dynamic or evolving expert systems.