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Writing correct Knowledge Representation & Reasoning solutions that mostly deal with artificial intelligence conceptualization is not a walk in the park. You are supposed to apply knowledge through logic between representation power and computational properties. Your solutions need to be highly supported by how reasoning algorithms and implementations operate together to support representation knowledge. Keep scrolling as we discover more about knowledge representation& reasoning practice problems for beginners.
Key Features
It represents real-world information for a computer to understand and then uses this knowledge to solve complex real-world problems such as communicating with humans in natural language.
Knowledge representation in AI is more than just storing data in a database; it also enables a machine to learn from that knowledge and behave intelligently like a human.
Coverage means that the KR covers a wide range of topics. The KR cannot determine anything or resolve ambiguities without a broad coverage.
Humans can understand it. Because KR is considered a natural language, the logic should flow freely. It should support modularity and class hierarchies.
Consistency. If John closed the door, this can also be interpreted as John closing the door. The KR can eliminate redundant or conflicting knowledge by being consistent.
KR systems must be simple to use. Each domain or system must be able to identify events and decipher how various components react to them.
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What is Knowledge Representation and Reasoning?
In artificial intelligence, knowledge representation implicates the study of intentions, beliefs, and judgments on the intelligent agent, which are expressed appropriately for automated reasoning. Therefore, the computer’s knowledge representation and reasoning depicted information from the real world in understanding and utilizing knowledge in solving complicated real-life challenges.
Some Of The Areas Focused On Under Knowledge Representations And Reasoning Are;
- Events – These are actions that do occur in the real world.
- Meta-knowledge – This is the knowledge about what we know
- Knowledge-bases – It’s the central critical aspect of knowledge-based agents. It’s built up with a group of sentences as a technical term.
- Performance – This expounds more on behavior that depicts a knowledge about how to do things.
- Facts –Truths about the real world and what we represent
- Events – These are actions that occur in the real world.
Types of Knowledge under Representation and Reasoning
1. Procedural Knowledge -
It’s popularly known as imperative knowledge responsible for knowing what to do. It’s versatile to be applied in any task, but there are rules, agendas, and strategies it has to abide by.
2. Heuristic Knowledge -
Heuristic knowledge represents the knowledge of some experts in a subject. This knowledge field operates under the rules of a thumb based on past experiences and awareness of approaches that are good to work with but not guaranteed.
3. Structural Knowledge -
Structural knowledge is basic to problem-solving, describing various concepts as part of, kind of, and grouping of something. It revolves around showcasing the relationship that exists between objects or concepts.
4. Declarative Knowledge -
Declarative knowledge is all about knowing about something. This is all about objects, facts, and concepts. In most instances, it is expressed in declarative sentences, implying simpler than procedural language.
Topics Programming Homework Tutors Cover Under Online knowledge representation& reasoning Assignment Help
Our Ph.D. Qualified Experts Can Proficiently Help You In The Following Areas;
- Fragments of First-Order Logic
- Reasoning in description logic
- Description logic as a knowledge representation language
- Lightweight description logic
- Ontologies and ontology languages
- Decidable fragments of first-order logic for knowledge representation
- Non-monotonic logics
- Stable model semantics
- Classical vs. non-monotonic logic
- KR&R with Propositional and First Order Logic
- Fundamentals of knowledge-based technologies and knowledge presentation
- Propositional logic
- Representing knowledge in first-order predicate logic
- Limitations of propositional and first-order predicate logic
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