Artificial Intelligence Knowledge Representation

Artificial Intelligence Knowledge Representation. Fundamental problem of designing a knowledge representation language is the fundamental tradeoff between: ◦ (1) a language that is expressive enough to represent the important objects and relations in a problem domain, yet ◦ (2) allows for a tractable (i.e., efficient) means of reasoning and answering questions about implicit information in a reasonable amount of time  Logic is a well-studied, general-purpose language for describing what's true and false in the world, along with mechanical procedures that can operate on sentences in the language to perform reasoning (i.e., to determine what "implicitly follows" from what is explicitly represented) 5  A framework for storing knowledge and manipulating knowledge Or  A set of syntactic and semantic conventions that make it possible to describe things  There is always a relationship between the form in which knowledge is represented and the way in which the knowledge is used. ◦ You can use domain-specific or general-purpose representation. 6  Let us first consider what kinds of knowledge might need to be represented in AI systems: ◦ Objects --- Facts about objects in our world domain. have wheels, cows are herbivores. ◦ Events -- Actions that occur in our world. e.g. The power-sharing deal was sealed on 28th February 2008 ◦ Performance -- A behavior like playing volleyball involves knowledge about how to do things like serve, boost, spike and block. ◦ Meta-knowledge -- knowledge about what we know. 7  Thus in solving problems in AI we must represent knowledge and  There are two entities to deal with, i.e. facts and representation of these. ◦ Facts -- truths about the real world and what we represent.  This can be regarded as the knowledge level ◦ Representation of the facts which we manipulate.  This can be regarded as the symbol levelsince we usually define the representation in terms of symbols that can be manipulated by programs. 8 Knowledge may be used in the following ways:  Learning ◦ acquiring knowledge. This is more than simply adding new facts to a knowledge base. New data may have to be classifiedprior to storage for easy retrieval, etc.. Interaction and inferencewith existing facts to avoid redundancy and replication in the knowledge and and also so that facts can be updated.