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Introduction to Expert System Unit I Prepared by: Prof. Dr. Mubeen Ahmed Khan Assistant Professor Department of CSE, Medicaps University Indore What is an Expert System? An Expert System is a computer program (software) that uses artificial intelligence (AI) to reproduce the judgment of a human with expert knowledge in a particular field. A good Expert System solves a problem accurately, quickly and is easy to use. It doesn’t necessarily require technical wizardry. AI jargon can mask a bad Expert System. Think of an Expert System as preserving the expertise of humans in a database of knowledge. An Expert System attempts to act like a human expert on a particular subject area. The data in the knowledge base is added by humans that are experts in a particular domain and the Expert System is used by a non-expert user to acquire information. Expert Systems are often used to help non-experts when a human expert is too expensive, the results too slow if use a human(s), error rate too high with a human(s), unintentional human bias, or it is difficult for a person to reach the location. Components of an Expert System Knowledge base This is where the knowledge (information) is stored. It is created from information provided by human experts. It is a collection of facts and rules. The better the quality of the information and the understanding of the problems for the end user the better the results. Inference engine This acts like a search engine, examining the knowledge base for information that matches the user’s query/search. User interface When you tap on the call button on your phone that is part of the user-interface. It’s the front end that you can see and interact with. The best ones are easy to use. A good user interface allows non-expert users to query the Expert System (ask a question) and to receive advice (an answer that is easy to understand). What an Expert Systems is capable of doing 1.Advising 2.Instructing and assisting human in decision making 3.Demonstrating 4.Deriving a solution 5.Diagnosing 6.Explaining 7.Interpreting input 8.Predicting results 9.Justifying the conclusion 10.Suggesting alternative options to a problem An expert system is a computer program designed to imitate a human expert, mimicking the knowledge base and the decision making process of a human expert. An expert system is different from conventional programs because it can explain its behavior to the human expert and receive new information without new programming. An expert system is a computer program designed to imitate a human expert, mimicking the knowledge base and the decision making process of a human expert. An expert system is different from conventional programs because it can explain its behavior to the human expert and receive new information without new programming. Expert systems have five fundamental parts: Knowledge base: Contains facts, rules, procedures, and intrinsic data relevant to a particular domain. Inference engine: Processes information in the knowledge base and applies it to specific situations or problems. It uses reasoning techniques like forward chaining and backward chaining to derive conclusions and make decisions. Explanation component: Provides explanations. User interface: Allows for user interaction. Acquisition component: Allows the expert system to acquire more knowledge from various sources and store it in the knowledge base. Other features of expert systems include: uncertainty handling, modularity, and narrow focus on a specific domain for decision support. Understanding Artificial Intelligence Understanding artificial intelligence (AI) can be a daunting task, but it is becoming increasingly necessary in today's technology- driven world. AI refers to machines or computers that are programmed to think and act like humans, making decisions and carrying out tasks without direct human intervention. This advanced technology has the potential to revolutionize industries such as healthcare, finance, and transportation. However, to use it effectively and ethically, it is crucial to have a strong understanding of AI principles and limitations. Types of AI AI has been a buzzword in the tech industry for quite some time now. But what exactly is AI and what types of AI are there? There are various types of AI, including: Rule-based AI: This type of AI uses predefined rules and logic to make decisions. Machine learning: With machine learning, algorithms are used to analyze and learn from large amounts of data to make predictions or decisions. Natural language processing (NLP): NLP enables machines to understand and process human language. Computer Vision: This type of AI allows machines to identify and interpret visual data. Robotics: Robotics combines different aspects of AI, such as machine learning and NLP, with physical machinery. Evolution of AI Development The development of artificial intelligence (AI) has come a long way since its inception. In the early days, AI was primarily focused on performing tasks that required human-like intelligence, such as problem-solving and decision-making. However, with advancements in technology and increased computing power, AI has evolved into much more than that. Today, AI is used in various industries, from healthcare to finance to transportation. It has also become increasingly sophisticated, with the ability to learn from data and improve over time. This evolution of AI has opened up new possibilities and opportunities for businesses and society as a whole, leading to rapid growth in its development. As we continue to push the boundaries of what AI can do, it is an exciting time to witness its evolution and potential for the future. Key Concepts in AI Development The key concepts of AI development revolve around creating intelligent machines that can perform tasks typically done by humans, such as problem-solving, decision-making, and learning. These machines require complex algorithms and large amounts of data to learn from and improve their performance over time. Data Collection and Preprocessing Machine Learning Algorithms Deep Learning Techniques Data Collection and Preprocessing In the realm of AI development, the significance of high-quality data cannot be overstated. At the very foundation of creating intelligent systems that can learn, predict, and make informed decisions is the collection and preprocessing of data. Collecting data is the first critical step, which involves gathering relevant information from various sources that can be used to train machine learning models. This data must then undergo a crucial phase known as preprocessing, where it is cleaned, normalized, and transformed to ensure consistency and accuracy. By meticulously collecting and preprocessing data, developers can lay a solid foundation for AI systems, enabling them to operate more efficiently and effectively. This process not only enhances the model's performance but also significantly reduces the chances of biases and errors, leading to more reliable and trustworthy AI solutions. Machine Learning Algorithms Understanding the different machine learning algorithms is pivotal in AI development for creating intelligent systems that can learn from data and improve over time. First, we have supervised learning, where the algorithm learns from labeled data, making predictions or decisions based on input-output pairs. It's like teaching a child with examples. Next, unsupervised learning involves training an algorithm on data without explicit instructions, allowing it to identify patterns and relationships on its own—a bit like solving a puzzle without the picture on the box. Lastly, reinforcement learning takes a different approach by rewarding the algorithm for making correct decisions and penalizing it for errors, akin to training a pet with treats and corrections. Each of these learning algorithms plays a crucial role in the development of AI, enabling machines to tackle complex tasks, from voice recognition to autonomous driving. Deep Learning Techniques Deep learning is revolutionizing AI development through sophisticated techniques, notably neural networks. At the heart of this innovation are Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). CNNs are particularly adept at processing visual information, making them indispensable for tasks like image and video recognition. They simulate how the human brain processes visual data, identifying patterns and features with remarkable accuracy. RNNs, on the other hand, excel in handling sequential data, such as text or speech. Their architecture allows them to remember information from previous inputs, making them ideal for language translation, sentiment analysis, and speech recognition. Together, CNNs and RNNs form the backbone of deep learning techniques, pushing the boundaries of what AI can achieve. Thank You Components of Expert Systems Prepared by: Prof. Dr. Mubeen Ahmed Khan Assistant Professor, Medicaps University Indore Programming Languages and Tools for AI Development As AI continues to advance and evolve, the demand for skilled developers in this field is rapidly increasing. With that being said, it's important to be familiar with the various languages and tools used in AI development. Popular Languages for AI Development Among the most frequently utilized programming languages are Python, Java, and C++. These languages stand out for their remarkable flexibility and powerful capability to manage extensive data sets efficiently, rendering them perfectly suited for complex tasks such as machine learning and data analysis. Python, for instance, is celebrated for its readability and concise syntax, making it a favorite among beginners and experts alike. Java, known for its portability, allows developers to write code once and run it anywhere, which is a significant advantage in diverse environments. C++, with its blend of low-level and high-level features, offers the speed necessary for performance-critical applications. Each of these languages contributes uniquely to the vast ecosystem of programming, enablingPrpareddevelopers andA. Khan by: Prof. Dr. Mubeen researchers to push the2 boundaries of what's possible in machine learning and data analysis. Frameworks and Libraries In the realm of tools for constructing neural networks and deep learning models, TensorFlow, PyTorch, and Keras stand out due to their robust features and extensive community support. TensorFlow, developed by Google, offers a comprehensive ecosystem of tools and libraries that facilitate both research and production in machine learning. PyTorch, known for its simplicity and ease of use, has been rapidly adopted for its dynamic computational graph that allows for flexibility in building complex models. Keras, on the other hand, acts as a high-level neural network API, designed for human beings, not machines, making it exceptionally user-friendly. Moreover, for developers seeking ready-made AI capabilities, IBM Watson, Google Cloud AI, and Microsoft Cognitive Services present a range of pre-built AI solutions. These platforms offer various AI services that can be seamlessly integrated into applications, enabling functionalities such as natural language processing, computer vision, and predictive analytics. IBM Watson is renowned for its powerful question-answering capabilities, Google Cloud AI provides a suite of machine learning services that leverage Google’s cutting-edge technologies, and Microsoft Cognitive Services brings AI within reach through a collection of APIs that allow systems to see, hear, speak, understand, and interpret human needs. These tools and platforms significantly reduce the complexity and time required to implement AI solutions, making advanced Prpared by: AI more Prof. Dr. Mubeen accessible to developers and3 A. Khan businesses alike. Ethical Considerations in AI Development Impact on Society Bias in Data and Algorithms Privacy Concerns Prpared by: Prof. Dr. Mubeen A. Khan 4 Impact on Society The impact of AI on society is undeniable and far-reaching. As machines become more advanced and able to perform complex tasks, the question arises: what will be the role of humans in a world dominated by AI? On one hand, AI has the potential to greatly improve our lives, making processes more efficient and freeing up time for us to focus on more creative endeavors. However, there are also concerns about job displacement and the ethical implications of relying heavily on AI for decision-making. As we continue to integrate AI into various industries and our daily lives, it is crucial that we consider its impact on society as a whole and strive for responsible implementation. Prpared by: Prof. Dr. Mubeen A. Khan 5 Bias in Data and Algorithms In recent years, there has been a growing concern over the potential bias in data and algorithms used in the development of AI. Data is the foundation of AI, as algorithms are trained on vast amounts of it to make decisions. However, this data is not always neutral and can reflect societal biases and inequalities. This means that the AI systems developed from this data can also inherit these biases, leading to discriminatory outcomes. For example, facial recognition technology has been shown to have racial and gender biases due to the lack of diversity in the training dataset. This not only results in inaccurate identification but also perpetuates systemic discrimination. It is crucial for developers and researchers to actively address and mitigate bias in both data collection and algorithm design to ensure fair and ethical AI. Prpared by: Prof. Dr. Mubeen A. Khan 6 Privacy Concerns As AI systems collect massive amounts of data and learn from it, there is a risk of invasion of privacy. These systems can gather personal information such as location data, browsing history, and even facial recognition without the user's consent. This raises concerns about how this information will be used and who will have access to it. Additionally, there is also a potential for bias in AI algorithms that may perpetuate discrimination and violate individuals' privacy rights. As AI continues to advance and become more integrated into our lives, it is crucial to address these privacy concerns and ensure that adequate measures are in place to protect individuals' personal information. Prpared by: Prof. Dr. Mubeen A. Khan 7 Challenges Faced in AI Development The development of AI is not without its challenges. These challenges require careful consideration and collaboration amongst experts from various fields to ensure the responsible and beneficial development of artificial intelligence. Lack of Quality Data Technical Difficulties Interpretation of Results Prpared by: Prof. Dr. Mubeen A. Khan 8 Lack of Quality Data One of the biggest challenges in AI development is the lack of quality data. In order for AI algorithms to accurately learn and make decisions, they require a large amount of high-quality data. However, obtaining such data can be a difficult and time-consuming process. Additionally, there is often a lack of diversity in the data sets used for training AI models, leading to biased or incomplete results. This not only limits the capabilities of AI systems but also raises ethical concerns about their use. Without access to reliable and diverse data, it becomes challenging for developers to create robust and trustworthy artificial intelligence systems. Therefore, addressing the problem of inadequate data is crucial for advancements in the field of AI and its successful implementation in various industries. Prpared by: Prof. Dr. Mubeen A. Khan 9 Technical Difficulties As AI continues to advance and become more complex, it has become increasingly difficult to troubleshoot issues and ensure that all components are functioning properly. This can lead to delays in development and setbacks in achieving desired results. Technical difficulties can also hinder the deployment and implementation of AI systems, which can be crucial for businesses and organizations looking to utilize this technology. Therefore, it is important for developers to continuously improve their troubleshooting skills and stay up-to-date with the latest advancements in technology to overcome these challenges and drive progress in AI development. Prpared by: Prof. Dr. Mubeen A. Khan 10 Interpretation of Results One of the major obstacles in AI development is the interpretation of results. With the increasing complexity and sophistication of AI algorithms, it becomes difficult for developers to fully understand and explain how a particular decision or prediction was made by the AI system. This lack of interpretability can lead to mistrust and skepticism towards AI technology. One example of this problem is in the use of deep learning algorithms, which have been proven to be highly effective in tasks such as image recognition and natural language processing. However, due to their complex nature, it is often difficult to understand how they arrive at their decisions. This makes it challenging for developers to identify and correct any biases that may exist within the algorithm. Another example is in the use of autonomous vehicles, where the decision-making process behind accidents or errors can be difficult to interpret. This not only raises ethical concerns but also poses a challenge in ensuring the safety and reliability of these vehicles. Moreover, there have been instances where AI systems have made incorrect predictions or decisions due to biased data sets or flawed algorithms. The lack of interpretability makes it challenging for developers to identify and rectify these issues, leading to potential harm and unintended consequences. While AI technology has shown great potential in various industries, its lack of interpretability remains a significant obstacle to its development. It is crucial for developers to address this issue by incorporating transparency and explainability into AI systems to build trust and ensure responsible deployment of this powerful technology Prpared by: Prof. Dr. Mubeen A. Khan 11 Understanding the Role of Human Expertise in AI Development The journey through AI development is not just a testament to technological advancement but also to the indispensable role of human expertise. From grasping the basics to applying intricate deep learning techniques, the value of deep human understanding in mastering core concepts, types of AI, and their practical applications cannot be overstated. As we venture into this evolving landscape, it's the human intellect and creativity that empower developers to leverage advancements in machine learning algorithms and deep learning. This synergy between human ingenuity and artificial intelligence is what enables the creation of intelligent systems capable of transforming industries and enriching our daily lives. Whether it's improving healthcare, revolutionizing finance, or pioneering technological innovation across various sectors, AI's potential is truly unleashed through the collaboration between humans and machines. As we continue to explore and enhance the capabilities of AI, it's clear that the future is bright, brimming with opportunities for significant breakthroughs and innovative solutions, all driven by the critical influence of human expertise in AI development. Have questions about how AI could integrate with your software development process? Contact the team at DragonSpears! Prpared by: Prof. Dr. Mubeen A. Khan 12 Thank You Prpared by: Prof. Dr. Mubeen A. Khan 13 Components of Expert System Important Expert System Components User A facility for the user to Interface interact with the Expert System Inference Reasoning (Thinking). Engine Makes logical deductions based upon the knowledge in the KB. Knowledge Base Contains the domain knowledge All Expert System Components Knowledge Base Inference Engine To be classified as an User Interface ‘expert system’, the system must be able Working Memory / Blackboard / Workplace to explain the – A global database of facts used by reasoning the system process. Knowledge AcquisitionThat’s Facilitythe difference – An (automatic) way to acquire knowledge with knowledge Explanation Facility based systems – Explains reasoning of the system to the user Knowledge Base The knowledge base contains the domain knowledge necessary for understanding, formulating, and solving problems Two Basic Knowledge Fact: IfBase NewElements Amsterdam Heuristic: is the capital – Facts: FactualEngland knowledge is that of knowledge Patriots of the task domain that is widely shared, typically the win foundBowl Netherlands. Super in textbooks or journals, and commonly agreed upon by those knowledgeable in the particular rdfact: field. for Not 3a straightNew time, England – Heuristics: Heuristic they isare Patriots knowledge the less strictly defined, relies more on empiricalprobably have data, morethe the best bestknowledge of performance judgmental team in the NFL Knowledge Acquisition Methods Manual (Interviews) – Knowledge engineer interviews domain expert(s) Semiautomatic (Expert-driven) Automatic (Computer Aided) Most Common Knowledge Acquisition: Face-to-face Interviews Knowledge Representation Knowledge Representation deals with the formal modeling of expert knowledge in a computer program. Important knowledge representation schemas: – Production Rules (Expert systems that represent domain knowledge using production rules are called rule-based expert systems) – Frames – Semantic objects Knowledge Representation Must Support: – Acquiring (new) knowledge – Retrieving knowledge – Reasoning with knowledge Production Rules Condition-Action Pairs: – A RULE consists of an IF part and a THEN part (also called a condition and an action). if the IF part of the rule is satisfied; consequently, the THEN part can be concluded, or its problem-solving action taken. Rules represent a model of actual human behavior Rules represent an autonomous chunk of expertise When combined, these chunks can lead to new conclusions Advantages & Limitations of Rules Advantage – Easy to understand (natural form of knowledge) – Easy to derive inference and explanations – Easy to modify and maintain Limitations – Complex knowledge requires many rules – Search limitations in systems with many rules – Maintaining rule-based systems is difficult because of inter-dependencies between rules Demonstration of Rule-Based Expert Systems Command & Conquer Generals My own Expert System in Wargus Rules in Wargus { id = 1, name = "build townhall", preconditions = {hasTownhall(),hasBarracks()}, actions = { function() return AiNeed(AiCityCenter()) end, function() return AiSet(AiWorker(), 1) end, function() return AiWait(AiCityCenter()) end, function() return AiSet(AiWorker(), 15) end, function() return AiNeed(AiBarracks()) end, } }, { id = 2, name = "build blacksmith", preconditions = {hasTownhall(),hasBarracks()}, etc. Question: how would you encode domain knowledge for Wargus? ‘Study’ strategy guides for Warcraft 2 (manual) Run machine learning experiments to discover new strong rules (automatic) Allow experts (i.e., hardcore gamers) to add rules (semi-automatic) Inference Mechanisms Examine the knowledge base to answer questions, solve problems or make decisions within the domain Inference mechanism types: – Theorem provers or logic programming language (e.g., Prolog) – Production systems (rule-based) – Frame Systems and semantic networks – Description Logic systems Inference Engine in Rule-Based Systems Inferencing with Rules: – Check every rule in the knowledge base in a forward (Forward Chaining) or backward (Backward Chaining ) direction – Firing a rule: When all of the rule's hypotheses (the “IF parts”) are satisfied – Continues until no more rules can fire, or until a goal is achieved Forward Chaining Systems Forward-chaining systems (data-driven) simply fire rules whenever the rules’ IF parts are satisfied. A forward-chaining rule based system contains two basic components: – A collection of rules. Rules represent possible actions to take when specified conditions hold on items in the working memory. – A collection of facts or assumptions that the rules operate on (working memory). The rules actions continuously update (adding or deleting facts) the working memory Forward Chaining Operations The execution cycle is – Match phase: Examine the rules to find one whose IF part is satisfied by the current contents of Working memory (the current state) – Conflict resolution phase: Out of all ‘matched’ rules, decide which rule to execute (Specificity, Recency, Fired Rules) – Act phase: Fire applicable rule by adding to Working Memory the facts that are specified in the rule’s THEN part (changing the current state) – Repeat until there are no rules which apply. Forward Chaining Example Rules Working Memory 1. IF (ownTownhalls < 1) THEN ADD (ownTownhalls (ownTownhalls = 1) 0) ++) (ownBarracks = 1) 2. IF (ownTownhalls > 0) (ownLumbermill = 1)0) AND (ownBarracks > 0) (ownBlacksmith = 1) 0) AND (ownLumbermills < 1) THEN ADD (ownLumberMills ++) Rule Only 2&3 Rule No Rules 3. IF (ownTownhalls > 0) Only Rule 3 apply, assume 1 applies Apply. Done! AND (ownBarracks > 0) applies AND (ownBlacksmith < 1) we select 2 THEN ADD (ownBlacksmiths ++) Backward Chaining Systems Backward-chaining (goal-driven) systems start from a potential conclusion (hypothesis), then seek evidence that supports (or contradicts) it A backward-chaining rule based system contains three basic components: – A collection of rules. Rules represent possible actions to take when specified conditions hold on items in the working memory. – A collection of facts or assumptions that the rules operate on (working memory). The rules actions continuously update (adding or deleting facts) the working memory – A stack of goals, where a goal is simply a statement of something that the rules need to determine Backward Chaining Operations The execution cycle is – Start with goal state – Check the conclusions of the rules to find all rules that can satisfy the top goal on the stack – Select one of these rules; the preconditions of the selected rule will be set as new goals on the goal stack – System terminates if goal stack is empty Backward Chaining example Question: Does my Wargus opponent own a town hall? Statement: My Wargus opponent owns a town hall. Rule: If my Wargus opponent owns a lumber mill or black smith or barracks, then he owns a town hall. Backward Chaining: Check the rule base to see what has to be “true” for my opponent to own a town hall. If my Wargus opponent either owns a barracks, blacksmith or lumber mill, then we may conclude that he has a town hall. Explanation facilities Explain the reasoning process: why it asked some question, and how it reached some conclusion System: Is there gas in the fuel tank? User: Yes. System: Is it true that the engine turns over? User: No. System Is it true that the lights come on? User: Why? System: I am trying to prove: problem with battery. It has been established that it is not the case that the engine turns over. Therefore if it is not the case that the lights come on then there is a problem with battery. Is it true that the lights come on? User: No. System: I conclude that there is a problem with the battery. User: How? System: This follows from Rule 2: IF NOT engine_turns_over AND... Problems and Limitations of Expert Systems Knowledge is not always readily available Expertise can be hard to extract from humans Expert Systems work well only in a narrow domain of knowledge Knowledge engineers are rare and expensive Expert Systems are expensive to design & maintain Lack of trust by end-users (we are still dealing with a computer) Inability to learn Some Expert System Tools PROLOG – A logic programming language that uses backward chaining. CLIPS – – NASA took the forward chainingClips was capabilities and syntax of ART and introduced the "C Language Integrated Production System" (i.e., CLIPS) into the public domain. used in the OPS5 Microsoft game System – First AI language used for Production “Ages of (XCON) EMYCIN, Empires” – Is an expert shell for knowledge representation, reasoning, and explanation MOLE – A knowledge acquisition tools for acquiring and maintaining domain knowledge Some Expert System Examples MYCIN (1972-80) – MYCIN is an interactive program that diagnoses certain infectious diseases, prescribes antimicrobial therapy, and can explain its reasoning in detail PROSPECTOR – Provides advice on mineral exploration XCON – configure VAX computers DENDRAL (1965-83) – rule-based expert systems that analyzes molecular structure. Using a plan- generate-test search paradigm and data from mass spectrometry and other sources, DENDRAL proposes plausible candidate structures for new or unknown chemical compounds. Thank You Expert system with Conventional system Prepared by: Prof Dr. Mubeen Ahmed Khan Assistant Professor, Department of CSE Conventional systems are rule based systems. Rules are clearly defined and implemented in the programming language as to how the system should function and behave in certain condition. AI systems on the other hand are observation and learning based systems. They observe the surrounding ecosystem and the environment, the past data and how the system has responded in past to certain data. Based on this data, a pattern is established, rules are derived automatically and then systems follow these rules. The rules may evolve overtime based on the new data that system is constantly accumulating Prepared by: Dr. Mubeen A. Khan, Medicaps 2 University, Indore Prepared by: Dr. Mubeen A. Khan, Medicaps 3 University, Indore Artificial intelligence (AI) is distinct from traditional systems in a number of key respects. Acquiring knowledge and flexibility: Conventional systems: Comply with preset guidelines and directives, carrying out operations in a fixed and unchanging way. They have no capacity for learning or data adaptation. Artificial intelligence (AI) systems: May grow in performance and capabilities over time by learning from data and experience. Based on the data they have analyzed, they are able to modify their behavior and even come up with new ideas. Prepared by: Dr. Mubeen A. Khan, Medicaps 4 University, Indore Problem-solving and adaptability: Conventional systems: Designed to do a single task and ineffective in new circumstances. As technology advances, they frequently become outdated. AI systems: Able to manage a variety of tasks and adjust to novel situations. Students can use what they've learned to tackle a variety of issues, with differing degrees of success. Prepared by: Dr. Mubeen A. Khan, Medicaps 5 University, Indore Decision-making and independence: Conventional systems: Need ongoing human supervision and involvement. They are unable to make independent decisions and function within preset parameters. AI systems: Are capable of operating with different levels of autonomy, making choices and acting within predetermined boundaries. As a result, they are able to manage challenging circumstances and react quickly to developments in the present. Prepared by: Dr. Mubeen A. Khan, Medicaps 6 University, Indore Capabilities similar to those of humans: Conventional systems: They are not as capable of processing and comprehending data as people are. They have trouble interacting in plain language and lack common sense reasoning. AI systems: In certain domains, such as speech recognition, image comprehension, and natural language processing, some sophisticated AI systems have human-like abilities. This creates opportunities for interactions with technology to become more instinctive and natural. Prepared by: Dr. Mubeen A. Khan, Medicaps 7 University, Indore All things considered, AI signifies a paradigm change in the way we create and engage with systems. It progresses from preprogrammed functionality to dynamic, adaptive, and intelligent robots capable of reasoning, learning, and decision-making in intricate contexts. Prepared by: Dr. Mubeen A. Khan, Medicaps 8 University, Indore Difference between AI and conventional system An expert system is divided into two subsystems: 1) a knowledge base, which represents facts and rules; and 2) an inference engine, which applies the rules to the known facts to deduce new facts, and can include explaining and debugging abilities. Conventional systems are unable to explain a specific solution to a problem. These systems aim to provide straightforward solutions. However, expert systems can justify why certain information is required during a process and Prepared by: how Dr. Mubeen A. Khan, Medicaps 9 a specific result was achieved.University, Indore THANK YOU Prepared by: Dr. Mubeen A. Khan, Medicaps 10 University, Indore

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