Expert Systems Part A PDF

INTRODUCTI ON TO EXPERT SYSTEM "With the new day comes new strength and new thoughts. Either you run the day or the day runs you." - Eleanor Roosevelt  AGENDA Part-A Introduction Characteristics and elements of expert system Expert system applicat...

INTRODUCTI ON TO EXPERT SYSTEM "With the new day comes new strength and new thoughts. Either you run the day or the day runs you." - Eleanor Roosevelt  AGENDA Part-A Introduction Characteristics and elements of expert system Expert system application and domains Development of expert system Advantages of expert system Part-B Expert system Tools Part-C Design of Expert System 2  INTRODUCTION  An expert system is a type of artificial intelligence (AI) software designed to simulate the decision-making ability of a human expert in a specific domain. or  Expert Systems (ES) are computer programs that try to replicate knowledge and skills of human experts in some area, and then solve problems in this area (the way human experts would).  These systems use a knowledge base filled with domain-specific information and rules to interpret and solve complex problems  Expert systems are widely used in fields such as medical diagnosis, accounting, coding, and even in games.  HISTORICAL BACKGROUND  1943 Post, E. L. proved that any computable problem can be solved using a set of IF–THEN rules.  1961 GENERAL PROBLEM SOLVER (GPS) by A. Newell and H. Simon.  1969 DENDRAL (Feigenbaum, Buchanan, Lederberg) was the first system that showed the importance of domain–specific knowledge (expertise).  1970s MYCIN (Buchanan & Shortliffe) medical diagnosis system introduced the use of certainty factors.  1982 R1 (aka XCON) by McDermott was the first commercial ES (by 1986 it was saving DEC $40 millions p.a.). 5 WHY DO YOU NEED EXPERT SYSTEMS?  Today’s world requires more and more experts in the ever-growing technological feats that humans are achieving.  The important thing here is to see if you can put the power of computing to good use.  Expert systems in AI are the way computers replicate the knowledge and the skills of a person who’s an expert in a field. Some of the biggest advantages that Expert Systems provide us are these four aspects: o Maximum efficiency o Reliability o High-level understandability o Unbeatable performance This process of taking an expert human’s knowledge and adding high amounts of computation power to it has proved nothing but immensely beneficial in today’s world. 7  HUMAN EXPERTS VS EXPERT SYSTEMS HUMAN EXPERTS EXPERT SYSTEM Unpredictable Highly consistent Subject to fatigue, mood, and Provides consistent results, free cognitive biases from fatigue or emotional Perishable knowledge infl uences Decision-making can be slower Can process large amounts of data Can adapt to new, unforeseen quickly and consistently Performs well in situations it is situations and learn from mistakes programmed for, but cannot handle unfamiliar problems unless updated by humans 8  UNDERSTANDING EXPERT SYSTEM An expert system is AI software that uses knowledge stored in a knowledge base to solve complex problems, typically requiring a human expert. It preserves human expertise within its knowledge base. Expert systems can advise users. They can provide explanations on how they reached a particular conclusion or advice. The process of building an expert system is called Knowledge Engineering. Practitioners of this process are called Knowledge Engineers. Ensure the computer has all the required knowledge to solve 9  CHARACTERISTICS OF EXPERT SYSTEM Follow are the characteristics of an expert system. A human expert can change, but an expert system can last forever. It facilitates the distribution of human expertise. The expert system might incorporate knowledge from multiple human experts, which would increase the effectiveness of the answers. It lowers the expense of seeking advice from a specialist in various fields, including medical diagnosis. Instead of using standard procedural code, expert systems can handle complex issues by inferring new facts from known facts of knowledge, which are typically represented as if-then rules. 10  TYPES OF EXPERT SYSTEMS IN AI There are various types of expert systems with their unique strengths and weaknesses, and they are used in various applications in multiple industries.  Rule-based Expert Systems Rule-based expert systems are the most common type of expert system. They use a set of rules to reason about a problem and provide solutions or recommendations. These rules are created by human experts and are organized in a knowledge base. Example: MYCIN, an early system for diagnosing bacterial infections.  Fuzzy Logic Expert Systems Fuzzy logic expert systems use fuzzy logic to handle uncertainty and imprecision in data. Fuzzy logic is a mathematical framework that allows for degrees of truth instead of the traditional binary (true or false) approach. Fuzzy expert systems are used in product recommendation systems and image recognition applications. Example washing machines and air conditioners. 11  TYPES OF EXPERT SYSTEMS IN AI  Knowledge-based Expert Systems Knowledge-based expert systems use a knowledge base that contains facts and rules about a specific domain. These systems are designed to mimic the problem-solving capabilities of human experts. They use a knowledge inference engine to explain the problem and provide solutions. Example NLP  Neural Networks Expert Systems Neural network expert systems are designed to learn from data by adjusting the weights of their connections between neurons. They are used in speech recognition, image classification, and natural language processing applications. example image and speech recognition. 12  TYPES OF EXPERT SYSTEMS IN AI Neuro-Fuzzy Expert Systems o Integrate neural networks and fuzzy logic to combine the learning capabilities of neural networks with the handling of uncertainty and imprecision offered by fuzzy logic. o This hybrid approach helps in dealing with complex problems where both pattern recognition and uncertain reasoning are required. o Example: environmental conditions or financial forecasting models that handle both quantitative data and fuzzy inputs. 13  COMPONENTS AND ARCHITECTURE OF AN EXPERT SYSTEM  Knowledge Base: Contains facts, rules, and procedures specific to a domain. Stores data relevant for problem-solving.  Inference Engine:  Fetches relevant knowledge from the knowledge base. Interprets the knowledge to find a solution to the user’s problem. Applies rules to known facts to infer new facts. May include explanation and debugging capabilities. 14  COMPONENTS AND ARCHITECTURE OF AN EXPERT SYSTEM  Knowledge Acquisition and Learning Module:​ Allows the expert system to acquire new knowledge from various sources.​ Stores acquired knowledge in the knowledge base.​  User Interface:​ Enables a non-expert user to interact with the system and find solutions.​  Explanation Module:​ Provides explanations to users on how the system reached its conclusions. 15 COMPONENTS AND ARCHITECTURE OF AN EXPERT SYSTEM Architecture of Expert system 16  HOW EXPERT SYSTEMS WORK? Expert systems operate by following a structured approach: 1.Input Data: Users provide data or queries related to a specific problem or scenario. 2.Processing: The inference engine processes the input data using the rules in the knowledge base to generate conclusions or recommendations. 3.Output: The system presents the results or solutions to the user through the user interface. 4.Explanation: If applicable, the system explains how the conclusions were reached, providing insights into the reasoning process. “Never become so much of an expert that you stop gaining expertise. View life as a continuous learning experience.” - Denis Waitley 17  HOW EXPERT SYSTEMS WORK? Reasoning Strategies used by Inference Engine Forward Chaining and Backward Chaining, which are two fundamental methods for processing information and solving problems in an expert system: 1. Forward Chaining This is a data-driven reasoning approach where the system starts with the available facts and applies rules to infer new facts or conclusions. It’s typically used to predict outcomes or determine what will happen next. An example given is predicting stock market movements. 18 FORWARD CHAINING 19  BACKWARD CHAINING 2. Backward Chaining This is a goal-driven reasoning approach where the system starts with a hypothesis or a goal (something to prove) and works backward to determine which facts or conditions would support that conclusion. It’s often used to diagnose issues by determining the cause of an observed effect. The examples provided include diagnosing medical conditions like stomach pain, blood cancer, or dengue. 20  APPLICATIONS AND DOMAINS OF EXPERT SYSTEM  Medical Diagnosis: Assists doctors in diagnosing diseases based on symptoms and patient data. Financial Services:  Used for loan approvals, investment analysis, and fraud detection. Customer Support: Provides automated customer service by offering solutions to common queries. Manufacturing and Production: Helps optimize production processes and troubleshoot equipment issues. Weather Forecasting: Analyzes meteorological data to provide accurate weather predictions. Engineering Design: Aids in solving design-related problems and providing technical solutions. Education and Training: Used in intelligent tutoring systems to provide personalized learning experiences. Agriculture: Helps in managing crop planning, pest control, and soil management. Legal Advisory: Provides expert legal advice by analyzing laws and past cases. 21  DEVELOPMENT OF EXPERT SYSTEMS 1.Problem Identification: a) Define the specific problem domain where expert knowledge is needed. b) Determine the scope of the system and the nature of the problem it will solve. 2.Knowledge Acquisition: a) Gather knowledge from human experts, research papers, and other sources. b) Interview domain experts to collect rules, facts, and problem-solving strategies. 3.Knowledge Representation: a)Choose the appropriate form to represent the acquired knowledge, such as: 1.Rules (if-then statements) 2.Semantic networks 3.Frames 22  DEVELOPMENT OF EXPERT SYSTEMS 4. Building the Knowledge Base: Encode the collected knowledge into a structured format (e.g., facts and rules) that the system can use for inference. 5. Development of Inference Engine: a) Design or implement the system’s inference mechanism to apply rules and reason based on the knowledge base. b) Ensure it supports forward chaining (data-driven reasoning) or backward chaining (goal-driven reasoning). 6. Integration of Explanation Facility: Add an explanation module to justify the system’s reasoning and decision-making process. 23  DEVELOPMENT OF EXPERT SYSTEMS 7. User Interface Design: a) Develop an easy-to-use interface for non-expert users to interact with the system. b) Ensure clear input methods and understandable output formats. 8. Testing and Validation: c)Test the system using real-world scenarios to evaluate its accuracy and performance. d)Validate its results by comparing them to expert human decisions. 9. Knowledge Refinement and Maintenance: e)Continuously update and refine the system’s knowledge base as new knowledge becomes available. f)Regularly assess the system to ensure relevance and accuracy. 24  ADVANTAGES OF EXPERT SYSTEMS 1. Consistent Decision-Making 2. Availability 3. Cost-Effective 4. Speed and Efficiency 5. Preservation of Expertise 6. Handling Complex Data 7. Training and Education 8. Reduced Errors 9. Scalability 10.Explanation and Justification 11.No Emotional Influence 25 DIS ADVANTAGES OF EXPERT SYSTEM 1) LACK OF COMMON SENSE 2) LIMITED TO SPECIFIC DOMAINS 3) INABILITY TO LEARN OR ADAPT 4) HIGH DEVELOPMENT COSTS 5) DIFFICULTY IN KNOWLEDGE ACQUISITION 6) RIGID DECISION-MAKING 7) NO EMOTIONAL INTELLIGENCE 8) MAINTENANCE AND UPDATES 9) NOT SUITABLE FOR DYNAMIC ENVIRONMENTS 26  THANK YOU Dept. Of AI Lovely Professional University "Programming is breaking of one big impossible task into several very small possible tasks"