International Conference on Fuzzy Systems and Knowledge Discovery, p.p ,July 2011

 Abstract  Introduction  Approaches for handling uncertainties  Conclusion  Reference

 This paper provides an analytical overview and perspective concerning the major methods of dealing with uncertainties in interactive systems. In particular, we suggest approaches of using neural networks in dealing with uncertainties due to the incompleteness and inaccuracy in man-machine interaction.

 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

 For a long time, the Bayesian model has been the primary numerical approach for representation and inference with uncertainty. Several mathematical models that are different from the probability prospective have also been proposed. The main ones are Shafer- Dempster's Evidence Theory (Belief Function) [1][2].  A. Probability and Bayesian Theory. In all the practical developments to date, the Bayesian formula and probability values have been used as some kind of coefficients to augment deterministic knowledge represented by production rules [4]. Some intuitive methods for combination and propagation of these values have been suggested and used. One such case is the use of Certainty Factors (CF) in MYCIN. Rich also use a simplified CF approach in user modeling system GROUNDY [5].

 B. The Dempster-Shafer Theory of Evidence The Dempster-Shafer theory, proposed by Shafer [1], was developed within the framework of Dempster's work [9] on upper and lower probabilities induced by a multi-valued mapping. Like Bayesian theory, this theory relies on degrees of belief to represent uncertainty. However, it allows one to assign a degree of belief to subsets of hypotheses. According to the Dempster-Shafer theory, the feature of multivalued mapping is the fundamental reason for the inability of applying the well-known theorem of probability that determines the probability density of the image of one- to-one mapping [10].

 C. Fuzzy Sets and Possibility Theory The theory of possibility was proposed independently by Zadeh, as a development of fuzzy set theory, in order to handle vagueness inherent in some linguistic terms [2]. For a given set of hypotheses, a possibility distribution may be defined in a way that is very similar to that of a probability distribution. However, there is a qualitative difference between the probability and possibility of an event. The difference is that a high degree of possibility does not imply a high degree of probability, nor does a low degree of probability imply a low degree of possibility. However, an impossible event must also be improbable. More formally, Zadeh defined the concept of a possibility distribution.

 D. Theory of Endorsement A different approach to uncertainty representation was proposed by Cohen [10], 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.

 E. Assumption based reasoning and non-monotonic logic In the reasoned-assumptions approach proposed by Doyle [15], the uncertainty embedded in an implication rule is removed by listing all the exceptions to that rule. When this is not possible, assumptions are used to show the typicality of a value (i.e., default values) and defeasibility of a rule (i.e., liability to defeat of reason). In classical logic, if a proposition C can be derived from a set of propositions S, and if S is a subset of T, then C can also be derived from T. As a system's premises increase, its possible conclusions at least remain constant and more likely increase. Deductive systems with this property are called monotonic. This kind of logic lacks tools for describing how to revise a formal theory to deal with inconsistencies caused by new information. McDermott and Dole proposed a nonmonotonic logic to cope with this problem [16].

 F. Artificial Neural Networks Using artificial neural network models to establish profiles and stereotypes of users in interactive systems has been proposed and tested since 1996 [18]. Both supervised networks and unsupervised networks can be used to trace the characteristics of users and their tasks. Unsupervised networks help systems to categorize users based on the incomplete inputs about their domain knowledge. The supervised networks help systems to adapt their responses to users’ preferences and domain knowledge in performing tasks. Since the users’ domain knowledge and their behaviors are pattern-based, traditional nonmonotonic approaches have difficulties in pattern recognition and completion, which is where the artificial neural networks exhibit their merits.

 This paper analyzes approaches of handling them in an adaptive human computer interface. Each approach can only deal with a particular type of uncertainty problems effectively. The interface system needs more comprehensive approach for uncertainty management due to various sources of uncertainties in human machine dialog. Especially, since human-machine dialog tend to be contextdependent, the management of uncertainty must provide a pattern-formatted view for user modeling. In other word, a user modeling system must examine the user input based the context of the dialog to obtain a complete and consistent user profiles.