Expert Systems in AI

A branch of AI that focuses on creating computer systems that can mimic the decision-making and problem-solving abilities of human experts in specific domains.

A collection of facts, rules, and relationships that represent the knowledge of a specific domain.

The part of an expert system that uses the knowledge base to reason and draw conclusions.

A programming language designed specifically for symbolic reasoning, often used in expert systems.

The ability of a machine to learn from data without explicit programming.

A type of machine learning where the computer is given labeled data (examples with correct answers) to learn patterns and make predictions.

A type of machine learning where the computer learns from unlabeled data, discovering patterns and structures on its own.

A type of machine learning where the computer learns through interaction with the environment, receiving rewards or penalties based on its actions.

The quality of a set of logical propositions where no parts contradict each other, ensuring all propositions are true simultaneously.

A set of logical propositions where at least one statement cannot be true at the same time, resulting in a contradiction.

The process of determining whether a knowledge base is free of contradictions, ensuring the logical soundness of the system.

The increase in computational effort required to perform logical reasoning as the size of the knowledge base grows.

The ability to gather feedback on system functionalities from potential users early in the development process.

A programming language designed for expressing logical rules and performing reasoning processes, enabling the creation of expert systems.

A programming style where the programmer defines the desired outcome, but not the steps to reach it. The language interpreter figures out the steps.

Programming languages that express instructions in a sequence of steps for the computer to follow. Think of it like a recipe.

A language used to query relational databases. It focuses on specifying what data to retrieve, not how to get it.

The process of converting a programming language instruction into a sequence of steps that the computer understands. It's like translating a recipe into cooking steps.

The lowest level of computer representation, using only 0s and 1s.

Abstractions in programming hide complex details, making programming less technical and more human-readable.

The goal of using Prolog is not to become a programming expert in it, but to understand its logic-based approach and its impact on AI and programming language design.

A collection of facts and rules that define relationships between those facts.

Logic rules that can be applied to facts to derive new conclusions.

A question or statement that Prolog tries to prove true.

A Boolean function that assigns a truth value (true or false) to an object or concept.

A logical expression composed of literals, which are atomic statements.

A symbol that represents an unknown value, which Prolog tries to find.

A pattern that Prolog tries to match against facts and rules to find a solution to a query.

An autonomous entity that interacts with its environment, learns from experience, and makes goal-oriented decisions.

A function that determines the value of an agent's actions based on their contribution to achieving a specific goal.

The process of continually improving an agent's performance by learning from its interactions with the environment through trial and error.

A powerful AI system developed by DeepMind that uses reinforcement learning to master games like Go, chess, and shogi at superhuman levels.

Devices that can track vital signs like blood pressure and temperature, potentially providing health advice based on collected data.

An AI system that can leverage medical knowledge to suggest optimal prescription combinations based on multiple medical conditions.

An AI that helps manage patient appointments and reminders, ensuring timely care for a large number of patients.

A statement expressing a specific fact in Prolog, like 'lectures(Smith, DLMAIAI01)' stating Dr. Smith teaches DLMAIAI01.

A defined relationship in Prolog, represented by a name followed by a slash and the number of arguments it takes. Example: 'professor/1' is a predicate defining 'professor' with one argument.

A rule in Prolog that describes a relationship based on other existing facts or rules. Example: 'technicalCourse(X) :- engineeringCourse(X)' states any course that's an engineering course is also a technical course.

A question or request posed to a Prolog system, asking for information based on the defined facts and rules. Example: '?– lectures(Smith, DLMAIAI01)' asks if Smith teaches DLMAIAI01.

A specific part of a Prolog query or rule that the system attempts to prove or solve. Example: lectures(Smith, X) is a goal to find all courses taught by Smith.

A symbol used in Prolog to represent any value, allowing for flexibility in rules and queries. Example: 'X' in lectures(Smith, X) can represent any course taught by Smith.

Prolog was used to build a system, CASEy, for Boeing, aiding shop floor workers with electrical part application and proper procedures. This highlights Prolog's ability to handle complex knowledge-based tasks.

Prolog was used at Surrey University to develop systems for water utilities, specifically for water distribution and planning, especially during emergencies. This shows Prolog's application in real-world scenarios.

AI can now analyze website traffic and purchase data to predict individual customer preferences, enabling personalized product recommendations.

Chatbots are evolving beyond simple scripts. They now can recognize voices and use humor, empathy, and politeness to improve customer interactions and build relationships.

AI is changing how we segment markets, allowing for hyper-targeted marketing campaigns that focus on specific neighborhoods or even individual streets.

Devices like vacuum cleaners and translators are becoming more intelligent, using AI to learn user preferences and adapt their behavior accordingly.

AI is revolutionizing how we grade tests, enabling both numerical and verbal feedback. This provides a more comprehensive and informative assessment of student work.

Using technology like AI sensors to monitor a driver's health and detect conditions like drunk driving. The AI system is designed to help ensure driver safety and potentially prevent accidents.

Employing AI agents to analyze patient records and suggest the most effective drug combinations, minimizing negative side effects. The goal is to optimize treatment plans for multiple conditions.

Using AI pattern recognition to detect irregularities, both big and small, in financial transactions. This enables early fraud detection and helps prevent massive losses.

Leveraging AI to analyze various securities and investor profiles to build custom portfolios, offering personalized financial advice based on individual risk tolerance.

AI systems can aid in creating new products by analyzing the many design possibilities and offering suggestions for innovative solutions. This allows businesses to explore various options more efficiently.

Using personalized AI-driven tools in online learning to deliver tailored instruction, improving the effectiveness of online courses and making education more engaging.

AI systems can be used to monitor patient appointments and ensure that necessary check-ups are scheduled on time, especially when managing a large number of patients.

AI systems can help assemble complex objects like aircraft by analyzing intricate components and verifying the correctness of each step. This ensures that the final product is built accurately.