1.1.1 Declarative presentation of knowledge.
KR's job is not only to store and organize information, but also to imbue it with meaning and context. Thanks to symbolic representations, ontologies, semantic networks, and probabilistic frameworks, KBIAs integrate subject-specific knowledge, which allows them to navigate complex decision spaces, taking into account the main concepts of the subject domain.
Symbolic representation of knowledge. Predicate logic represents knowledge using predicates, variables, and logical operators. Examples are first-order logic and higher-order logic. Good for presenting structured and formal knowledge. A frame-based representation organizes knowledge as frames or objects with slots and attributes. Used to represent structured information and inheritance relationships. Semantic networks represent knowledge in the form of graphs - nodes (concepts) connected by connections (relations). Useful for fixing connections and associations between concepts.
Ontologies, knowledge graphs, descriptive logic offer languages and models for displaying structured knowledge. Ontology is a formal representation of concepts, relationships, and attributes within a subject area. Ontologies define a shared vocabulary and semantics that allow reasoning and interaction. Knowledge graphs use graph structures to represent concepts and relationships, enabling semantic inference and reasoning. Descriptive logic is a subset of formal logic used in ontologies to express relationships, constraints, and axioms.
Bayesian networks and Markov models are used for probabilistic representation of knowledge. Bayesian networks represent uncertain knowledge through probabilistic relationships between variables and are used to model probabilistic relationships. Markov models reflect sequential processes with probabilistic transitions. Used for tasks such as speech recognition, NLP and machine translation, etc.
Fuzzy logic and fuzzy sets. Fuzzy logic represents uncertainty in knowledge using linguistic terms and membership functions. Fuzzy sets reflect the degree of membership of concepts, allowing a gradual transition between categories.
Presentation of knowledge in time. Modal logic and temporal logic extend predicate logic to consider time and temporal relationships. Useful for modeling events, causality, and time constraints. Temporal databases store time-stamped data and enable querying and reasoning about temporal information.
Connectionist representation of knowledge is neural networks that offer approaches to display knowledge and dependencies in data using interconnected nodes (neurons). Used for ML and pattern recognition tasks.
Semantic Web technologies and knowledge representation languages such as Resource Description Framework (RDF) and Web Ontology Language (OWL) are used to create structured, machine-readable Web data. Combine data connectivity and semantic compatibility.
Each method of declarative knowledge representation has its advantages and limitations. The choice depends on the nature of the presented knowledge, the required reasoning capabilities and the specific field of application. In practice, hybrid approaches are often used, which combine different methods to overcome the limitations of some and use the advantages of others.
1.1.2 Procedural presentation of knowledge.
The procedural knowledge representation in KBIA displays information about the processes, procedures, actions, and sequences of steps required to achieve specific goals. Procedural knowledge is typically represented in a way that allows agents to perform actions and follow established procedures for completing tasks.
Production rules consist of a set of conditional statements (if-then rules) that define actions to be performed when certain conditions are met. Well suited for presenting expert knowledge and procedural information. They are used in rule-based expert systems and decision support systems.
Scenarios are structured representations of typical sequences of actions. They describe the steps required to complete tasks or achieve goals. Scenarios are used to represent procedural knowledge in cases where tasks have a predictable sequence of actions, such as NLP or planning.
Planning formalisms represent formal knowledge about actions and their relationships. These include representations such as STRIPS, ADL, and PDDL. Planning formalisms are used to define actions, their prerequisites, consequences, and relationships between actions. They provide automated planning and reasoning.
Process models use graphical notation (eg, flowcharts, Petri nets) to represent the flow of actions, decisions, and interactions in a process. Process models are widely used in business process management, document management systems, and industrial automation.
State transition diagrams represent system states and transitions between states based on specific events or actions. They are commonly used to model the behavior of systems that have discrete states and transitions.
Hybrid approaches combine different methods to represent procedural knowledge. For example, the combination of production rules with state transition diagrams. They offer flexibility in representing complex procedures that involve both logical reasoning and sequential actions.
Cognitive architectures such as Soar or ACT-R use production rules and other elements to represent procedural knowledge together with other types of knowledge.
The choice of methods for presenting procedural knowledge depends on the complexity of the tasks, the nature of the procedures, the need for formal justification, and the specific requirements of KBIA application. In many cases, a combination of methods is used to effectively capture and represent procedural knowledge.
1.1.3 Cognitive processes and reasoning.
KBIA reasoning processes include deduction, induction, abduction, and analogical reasoning. Agents process data stores, establish correlations and dependencies, make causal inferences, and bridge information gaps, allowing them to draw logical conclusions about the nuances of relationships between domain entities and processes.
Deductive reasoning provides specific conclusions from general principles or premises. KBIA uses deductive reasoning to apply rules, facts, and logical connections to arrive at accurate conclusions. For example, a medical diagnosis system may use deductive reasoning to infer a specific disease based on observed symptoms and medical knowledge.
Inductive reasoning involves drawing generalized conclusions from specific observations or cases, revealing patterns, trends, and relationships in the data. For example, a financial forecasting system can use inductive reasoning to predict market trends based on historical data.
Abductive reasoning draws a conclusion that provides the best explanation for observed phenomena, even if the explanation is not guaranteed to be true. KBIA uses abductive reasoning to generate hypotheses and educated guesses about root causes. For example, a diagnostic system may use abductive reasoning to suggest potential causes for a set of symptoms. Often used to make educated guesses or hypotheses about the underlying causes or mechanisms that may have led to an observed situation. Abductive reasoning is a key aspect of problem solving, hypothesis generation, and scientific discovery.
Reasoning by analogy involves recognizing similarities between different situations or cases and using knowledge from one domain to understand another. KBIA uses analogical reasoning to transfer knowledge and solutions from one context to another. For example, KBIA, which provides engineering design assistance, can build on past successful projects to create solutions for new challenges. Analogical reasoning enhances KBIA's ability to transfer ideas, solutions, and knowledge from familiar contexts to new and unfamiliar scenarios.
Common sense reasoning summarizes knowledge that people take for granted. Efforts are being made to develop KBIAs capable of making common-sense knowledge inferences to improve understanding of natural language and real-world scenarios.
Context-aware reasoning takes contextual information into account when making decisions. It allows you to analyze the surrounding context, including the user's history, preferences, and current situation, to create responses that match the created context. Context-sensitive reasoning increases the relevance and effectiveness of KBIA and human interaction.
Probabilistic reasoning handles uncertainty by assigning probabilities to various hypotheses or conclusions, and uses probability theory to model and quantify uncertain information. KBIAs use probabilistic reasoning to manage incomplete or conflicting information. They weigh the evidence and generate probabilistic estimates for decision making.
Meta-reasoning, also known as “thinking about thinking,” is higher-level reasoning that helps KBIA choose the most effective strategies, heuristics, and algorithms to achieve its goals. Presupposes the agent's reflection on his own reasoning processes. This helps determine the most effective approach to solving a particular problem, taking into account one's own strengths and limitations.
KBIA combines these different forms of reasoning to navigate complex decision spaces, adapt to changing contexts, and reach informed conclusions. By integrating these cognitive processes into its activities, the agent simulates human intelligence and helps solve complex problems in areas such as healthcare, finance, engineering, and more.