Abstract
To adapt users’ input and tasks an interactive system must be able to establish a set of assumptions about users’ profiles and task characteristics, which is often referred as user models. However, to develop a user model an interactive system needs to analyze users’ input and recognize the tasks and the ultimate goals users trying to achieve, which may involve a great deal of uncertainties. In this chapter the approaches for handling uncertainty are reviewed and analyzed. The purpose is to provide an analytical overview and perspective concerning the major methods that have been proposed to cope with uncertainties.
TopIntroduction
To adapt users’ input and tasks an interactive system must be able to establish a set of assumptions about users’ profiles and task characteristics, which is often referred as user models. However, to develop a user model an interactive system needs to analyze users’ input and recognize the tasks and the ultimate goals users trying to achieve, which may involve a great deal of uncertainties. In this chapter the approaches for handling uncertainty are reviewed and analyzed. The purpose is to provide an analytical overview and perspective concerning the major methods that have been proposed to cope with uncertainties.
Key Terms in this Chapter
Theory of Endorsement: An approach to represent uncertainty proposed by Cohen, which is based on a qualitative theory of “endorsement.” According to Cohen, the records of the factors relating to one’s certainty are called endorsements. Cohen’s model of endorsement is based on the explicit recording of the justifications for a statement, normally requiring a complex data structure of information about the source. Therefore, this approach maintains the uncertainty. The justification is classified according to the type of evidence for a proposition, the possible actions required to solve the uncertainty of that evidence, and other related features
Shafer-Dempster’s Evidence Theory: A mathematical theory of evidence based on belief functions and plausible reasoning, which is used to combine separate pieces of information (evidence) to calculate the probability of an event. The theory was developed by Arthur P. Dempster and Glenn Shafer
Bayesian Theory: Also known as Bayes’ rule or Bayes’ law. It is a result in probability theory, which relates the conditional and marginal probability distributions of random variables. In some interpretations of probability, Bayes’ theory tells how to update or revise beliefs in light of new evidences
Possibility Theory: A mathematical theory for dealing with certain types of uncertainty and is an alternative to probability theory. Professor Lotfi Zadeh first introduced possibility theory in 1978 as an extension of his theory of fuzzy sets and fuzzy logic.
Non-Monotonic Logic: A formal logic whose consequence relation is not monotonic. Most studied formal logics have a monotonic consequence relation, meaning that adding a formula to a theory never produces a reduction of its set of consequences. Intuitively, monotonicity indicates that learning a new piece of knowledge cannot reduce the set of what is known. A monotonic logic cannot handle various reasoning tasks such as reasoning by default
Default Reasoning: A non-monotonic logic proposed by Raymond Reiter to formalize reasoning with default assumptions. Default reasoningc can express facts like “by default, something is true” by contrast, standard logic can only express that something is true or that something is false. This is a problem because reasoning often involves facts that are true in the majority of cases but not always. A classical example is “birds typically fly”. This rule can be expressed in standard logic either by “all birds fly”, which is inconsistent with the fact that penguins do not fly, or by “all birds that are not penguins and not ostriches and ... fly”, which requires all exceptions to the rule to be specified. Default logic aims at formalizing inference rules like this one without explicitly mentioning all their exceptions
Truth Maintenance System: A knowledge representation method for representing both beliefs and their dependencies. The name truth maintenance is due to the ability of these systems to restore consistency. There are two major truth maintenance systems: single-context and multi-context truth maintenance. In single context systems, consistency is maintained among all facts in memory (database). Multi-context systems allow consistency to be relevant to a subset of facts in memory (a context) according to the history of logical inference. This is achieved by tagging each fact or deduction with its logical history. Multi-agent truth maintenance systems perform truth maintenance across multiple memories, often located on different machines