Knowledge Representation

Knowledge representation and reasoning is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of reasoning, such as the application of rules or the relations of sets and subsets.

Examples of knowledge representation formalisms include semantic nets, systems architecture, frames, rules, and ontologies. Examples of automated reasoning engines include inference engines, theorem provers, and classifiers.

Following are the kind of knowledge which needs to be represented in AI systems:

• Events: Events are the actions which occur in our world.
• Object: All the facts about objects in our world domain. E.g., Guitars contains strings, trumpets are brass instruments.
• Performance: It describe behavior which involves knowledge about how to do things.
• Meta-knowledge: It is knowledge about what we know.
• Facts: Facts are the truths about the real world and what we represent.
• Knowledge-Base: The central component of the knowledge-based agents is the knowledge base. It is represented as KB. The Knowledgebase is a group of the Sentences (Here, sentences are used as a technical term and not identical with the English language).

Types of knowledge

1. Declarative Knowledge:

• Declarative knowledge is to know about something.
• It includes concepts, facts, and objects.
• It is also called descriptive knowledge and expressed in declarative sentences.
• It is simpler than procedural language.

2. Procedural Knowledge

• It is also known as imperative knowledge.
• Procedural knowledge is a type of knowledge which is responsible for knowing how to do something.
• It can be directly applied to any task.
• It includes rules, strategies, procedures, agendas, etc.
• Procedural knowledge depends on the task on which it can be applied.

3. Meta-knowledge:

• Knowledge about the other types of knowledge is called Meta-knowledge.

4. Heuristic knowledge:

• Heuristic knowledge is representing knowledge of some experts in a filed or subject.
• Heuristic knowledge is rules of thumb based on previous experiences, awareness of approaches, and which are good to work but not guaranteed.

5. Structural knowledge:

• Structural knowledge is basic knowledge to problem-solving.
• It describes relationships between various concepts such as kind of, part of, and grouping of something.
• It describes the relationship that exists between concepts or objects.

Characteristics

Primitives. Semantic networks were one of the first knowledge representation primitives. Also, data structures and algorithms for general fast search. In this area, there is a strong overlap with research in data structures and algorithms in computer science. In early systems, the Lisp programming language, which was modelled after the lambda calculus, was often used as a form of functional knowledge representation. Frames and Rules were the next kind of primitive. Frame languages had various mechanisms for expressing and enforcing constraints on frame data. All data in frames are stored in slots. Slots are analogous to relations in entity-relation modelling and to object properties in object-oriented modelling. Another technique for primitives is to define languages that are modelled after First Order Logic (FOL). The most well-known example is Prolong, but there are also many special purpose theorems proving environments. These environments can validate logical models and can deduce new theories from existing models. Essentially, they automate the process a logician would go through in analyzing a model. Theorem proving technology had some specific practical applications in the areas of software engineering. For example, it is possible to prove that a software program rigidly adheres to a formal logical specification.

Meta-representation. This is also known as the issue of reflection in computer science. It refers to the capability of a formalism to have access to information about its own state. An example would be the meta-object protocol in Smalltalk and CLOS that gives developers run time access to the class objects and enables them to dynamically redefine the structure of the knowledge base even at run time. Meta-representation means the knowledge representation language is itself expressed in that language. For example, in most Frame based environments all frames would be instances of a frame class. That class object can be inspected at run time, so that the object can understand and even change its internal structure or the structure of other parts of the model. In rule-based environments, the rules were also usually instances of rule classes. Part of the meta protocol for rules were the meta rules that prioritized rule firing.

Incompleteness. Traditional logic requires additional axioms and constraints to deal with the real world as opposed to the world of mathematics. Also, it is often useful to associate degrees of confidence with a statement. I.e., not simply say “Socrates is Human” but rather “Socrates is Human with confidence 50%”. This was one of the early innovations from expert systems research which migrated to some commercial tools, the ability to associate certainty factors with rules and conclusions. Later research in this area is known as fuzzy logic.

Definitions and universals vs. facts and defaults. Universals are general statements about the world such as “All humans are mortal”. Facts are specific examples of universals such as “Socrates is a human and therefore mortal”. In logical terms definitions and universals are about universal quantification while facts and defaults are about existential quantifications. All forms of knowledge representation must deal with this aspect and most do so with some variant of set theory, modeling universals as sets and subsets and definitions as elements in those sets.

Non-monotonic Reasoning. Non-monotonic reasoning allows various kinds of hypothetical reasoning. The system associates facts asserted with the rules and facts used to justify them and as those facts change updates the dependent knowledge as well. In rule based systems this capability is known as a truth maintenance system.

Expressive Adequacy. The standard that Brachman and most AI researchers use to measure expressive adequacy is usually First Order Logic (FOL). Theoretical limitations mean that a full implementation of FOL is not practical. Researchers should be clear about how expressive (how much of full FOL expressive power) they intend their representation to be.

Reasoning efficiency. This refers to the run time efficiency of the system. The ability of the knowledge base to be updated and the reasoner to develop new inferences in a reasonable period of time. In some ways, this is the flip side of expressive adequacy. In general, the more powerful a representation, the more it has expressive adequacy, the less efficient its automated reasoning engine will be. Efficiency was often an issue, especially for early applications of knowledge representation technology. They were usually implemented in interpreted environments such as Lisp, which were slow compared to more traditional platforms of the time.