Plausible Reasoning

Plausible Reasoning is a foundational concept related to the W3C CogAI works on CognitiveAI-WIP. In-order to provide some background resources are provided via the GitHub link for this CG. In-order to get a more generic summary, i've queried ChatGPT.

ChatGPT Notes

My Question: Define Plausible Reasoning as it relates to Cognative AI Agents?

ChatGPT Answer:

Plausible reasoning refers to the ability of a cognitive AI agent to generate reasonable and likely explanations for a given situation or problem, based on the information and knowledge it has been trained on. It involves understanding the context of a problem and applying relevant knowledge and reasoning techniques to generate a plausible solution or explanation. This type of reasoning is important in tasks such as natural language understanding, decision making, and problem solving.

Plausible reasoning is a form of probabilistic reasoning, which means that the AI agent considers multiple possible explanations and assigns a probability to each one based on the available evidence. This allows the agent to deal with uncertainty and make decisions based on the best available information.

In natural language understanding, for example, a cognitive AI agent with plausible reasoning capabilities would be able to understand the meaning of a sentence, even if it contains ambiguities or missing information. It would generate multiple possible interpretations of the sentence and choose the most likely one based on the context and the available knowledge.

In decision making, a cognitive AI agent with plausible reasoning capabilities would be able to make informed decisions, even in situations where the available information is incomplete or uncertain. It would consider multiple possible outcomes and choose the one that is most likely to achieve the desired goal.

In summary, plausible reasoning allows cognitive AI agents to generate reasonable and likely explanations or solutions based on incomplete or uncertain information. It is an important aspect of artificial intelligence research and is crucial for building intelligent agents that can operate in real-world environments.

My Question:

what fields of mathematics are used to design cognitive AI Plausible Reasoning software systems that are designed to work with Semtantic Web technologies such as RDF and RDFS, etc.

ChatGPT Answer:

Cognitive AI systems that are designed to work with semantic web technologies such as RDF and RDFS typically use a combination of mathematical techniques from several fields, including:

1. Graph theory: RDF and RDFS use a graph-based data model, where nodes represent entities and edges represent relationships between entities. Graph theory is used to analyze and manipulate the structure of these graphs.
2. Logic: Logical reasoning is used to make inferences about the relationships between entities in the RDF graph. Description Logic (DL) is a subset of First-order Logic (FOL) that is often used for this purpose.
3. Probability and statistics: Plausible reasoning in AI systems often involve probability and statistics, as the agent must assign probabilities to different possible outcomes or explanations. Bayesian networks, Markov decision process and Gaussian Process are few examples that are used in this area.
4. Machine learning: Machine learning techniques are used to train the AI system to make predictions or inferences based on the RDF data. This could be supervised, unsupervised or reinforcement learning depending on the problem at hand.
5. Optimization: Optimization algorithms are used to find the best possible solution to a problem, given a set of constraints. Linear programming, convex optimization and other optimization techniques are used in AI systems.
6. Natural Language Processing (NLP): Many semantic web applications involve natural language text, and NLP techniques are used to extract meaning from the text and relate it to entities in the RDF graph. This may include techniques such as named entity recognition, part-of-speech tagging, and sentiment analysis.
7. Information retrieval: Information retrieval techniques are used to search and retrieve relevant information from the RDF graph. This may include techniques such as keyword-based search, semantic search, and graph-based search.
8. Knowledge representation: Techniques such as ontologies and conceptual modeling are used to represent knowledge in a structured and formal way, making it easier for the AI system to understand and reason about the data.
9. Distributed Systems: Some semantic web applications involve distributed data, and distributed systems techniques are used to manage and coordinate access to the data.
10. Cyber Security: As with any system that handle sensitive data, security is of prime importance. Techniques from cryptography and secure protocols are used to secure the communication and storage of data.

In summary, cognitive AI systems that work with semantic web technologies such as RDF and RDFS use a combination of mathematical techniques from various fields such as graph theory, logic, probability and statistics, machine learning, optimization, natural language processing, information retrieval, knowledge representation, distributed systems and cyber security to make inferences about the relationships between entities in the RDF graph and generate plausible explanations or solutions.

Cognitive AI systems that work with semantic web technologies such as RDF and RDFS use a combination of mathematical techniques from graph theory, logic, probability and statistics, machine learning and optimization to make inferences about the relationships between entities in the RDF graph and generate plausible explanations or solutions.