ARTIFICIAL INTELLIGENCE [INTELLIGENT AGENTS PARADIGM] Professor Janis Grundspenkis Riga Technical University Faculty of Computer Science and Information Technology Institute of Applied Computer Systems Department of Systems Theory and Design LAWS OF QUALITATIVE STRUCTURES, SYMBOL SYSTEM HYPOTHESIS AND KNOWLEDGE REPRESENTATION IN LOGIC

Laws of Qualitative Structures The study of logic and computers has revealed that intelligence resides in physical symbol systems. This is computer science’s most basic law of qualitative structures. Symbol systems are collections of patterns and processes, the latter being capable of producing, destroying and modifying the former.

Laws of Qualitative Structures The most important property of patterns is that they can designate objects, processes or other patterns, and that when they designate processes they can be interpreted. A second law of qualitative structure for artificial intelligence is that symbol systems solve problems by generating potential solutions and testing them, that is, by searching.

Laws of Qualitative Structures Solutions are usually sought by creating symbolic expressions and modifying them sequentially until they satisfy the conditions for a solution.

Symbol System Hypothesis Following Newell and Simon, intelligent activities, in either human or machine, is achieved through the use of: 1.Symbol patterns to represent significant aspects of a problem domain. 2.Operations on these patterns to generate potential solutions to problems. 3.Search to select a solution from among these possibilities.

Symbol System Hypothesis Thus, if intelligence derives only from the structure of a symbol system, then any medium that successfully implements the correct patterns and processes will achieve intelligence.

Description of Knowledge-Based System The knowledge level or epistemological level is the most abstract. The system can be described by saying what it knows. For example, one might be said to know that the Golden Gate Bridge links San Francisco and Marin County. The logical level is the level at which the knowledge is encoded into sentences. For example, the logical sentence may be Links(Golden Gate Bridge, San Francisco, Marin County)

Description of Knowledge-Based System The implementation level is the level that runs on the system arhictecture. It is the level at which there are physical representations of the sentences at the logical level. For example, a logical sentence could be represented in the knowledge base by the string contained in a list of strings; or by a “1” entry in a three dimensional table indexed by road links and location pairs; or by a complex set of pointers connecting machine addresses corresponding to the individual symbols The choice of implementation is very important to the efficient performance of the system, but it is irrelevant to the logical level and the knowledge level

Knowledge Representation Provided the syntax and semantics are defined precisely the language is called a logic. From the syntax and semantics an inference mechanism can be derived for an intelligent system that uses the language.

Knowledge Representation WorldFacts Follows Facts RepresentationSentences Entails Sentences Semantics

Knowledge Representation Sentences are physical configurations, thus reasoning must be a process of constructing new physical configurations from old ones. Proper reasoning should ensure that the new configurations represent facts that actually follow from the facts that the old configurations represent.

Knowledge Representation The connection between sentences and facts is provided by the semantics of the language. The property of one fact following from some other facts is mirrored by the property of one sentence being entailed by some other sentence. Logical inference generates new sentences that are entailed by existing sentences.

Knowledge Representation The semantics of the language determine the fact to which a given sentence refers. Facts are part of the world. It is important to distinguish between facts and their representations. Representations must be encoded in some way that can be physically stored within an intelligent system It is impossible to put the world inside a computer (nor it is possible to put it inside a human), so all reasoning mechanisms must operate on representations of facts, rather than on facts themselves.