Por un reaprender hedonista del cuerpo

In this chapter, we explored the background to context information with a view to understanding the status of approaches in the field to situation recognition. We explained the link between sensors, abstracted context and situation. Situ- ations are the states of the environment of interest to application. From look- ing at situation features identified in the literature, we noted that situations are hierarchical, dynamic with rich temporal features, and driven by uncertain sensor data and inference rules. These complexities drive the requirements for whatever reasoning technique is used for situation recognition. From review- ing the related work, it is clear that no single reasoning scheme is a panacea for situation recognition. Each approach has its own advantages and limitations, so different environments will suit different techniques. Our conclusions on the existing body of work on situation recognition are:

• No single approach to situation recognition is a solution for all scenar- ios. Each reasoning scheme is applicable in certain scenarios, depending upon such factors as the availability of training data, the requirement for humans to be able to understand the context reasoning process, the ex- pert understanding of causal links between lower level and higher level contexts and the requirement to identify multiple situations in a hierar- chy versus identifying from a ’flat’ set of situations.

• Some researchers provide actual recognition results for their recognition technique. However, it is not appropriate to compare situation recog- nition results across different experimental set-ups from different re- searchers because the sensors, accuracies, situations and complexities are specific to the environment set-up used in each case. Bao et al. [7], for ex- ample, note a difference in inference results of 95.6% in a lab environment versus 66.7% in a naturalistic setting. Direct comparison would require the same data-set to be used by multiple reasoning schemes, using the same methodology.

• Temporal factors are not catered for in existing Dempster-Shafer ap-

proaches have exploited temporal information in situation detection, where this temporal information includes relative times of situations, sequence of two or more situations, and duration of a situation. Dy- namic Bayesian approaches use sequences of states to boost recognition. Temporal logic approaches encodes temporal information such as situ- ation sequences, duration, overlaps and intervals between events to en- hance recognition. Within the scope of this thesis, our approach will aim to incorporate duration and time of occurrence of a situation into our Dempster-Shafer approach. Inclusion of other types of temporal infor- mation such as sequences and overlaps into our approach is desirable as indicated by its usefulness in other approaches [5, 56], but is outside the scope of the work in this thesis.

• Fuzzy sets are applicable if context descriptions are imprecise, and may be incorporated into with other reasoning schemes; We will use fuzzy sets to combine sensor quality with Dempster-Shafer theory, as described further in Chapter 4.

• Learning schemes are a useful technique for situation recognition where

training data is available. They implicitly deal with uncertainty by

blindly absorbing uncertainty of sensor and rule information into the probabilistic model. However, if training data is an issue, Dempster- Shafer theory is the only specification-based approach that has strong theoretical support for processing uncertainty in an environment.

Table 2.1 summarises the requirement for domain knowledge versus training data, and the functionality of the various techniques.

Our particular interest is in a Dempster-Shafer based approach because it is not reliant on training data, but can cater for uncertainty. Existing work using Dempster-Shafer theory does not address the dynamic nature of situation or the inclusion of sensor quality. In the next chapter, we describe the theoretical foundations for our evidence based reasoning approach.

CHAPTER

THREE

Creating evidence decision

networks using Dempster-Shafer

theory

In the previous chapter, we examined approaches to situation recognition. We will use Dempster-Shafer theory as the basis of our approach. To do this, we need to describe the process by which sensor data will be processed as evi- dence, and distributed across situations.

The purpose of this chapter is to explain the concepts of Dempster-Shafer the- ory and how we apply it to reasoning about situations. We define the evi- dence processing needed to determine situation occurrence based on sensor evidence, using a variety of evidential operations. In Section 3.2, we describe the basic elements of Dempster-Shafer theory: frames of discernment, mass functions, evidence fusion and sensor discounting. In Section 3.3, we explain how we document the relationships between sensors and situations using our own diagramming technique, situation Directed Acyclic Graphs (DAGs). We also explain the architecture of the evidence decision network. Section 3.4 ex- plains the evidential operations that will be required to process evidence for belief distribution and decision making. These operations consist of opera- tions from basic Dempster-Shafer theory, extensions to the theory by other researchers, and our own new operations where none exist to meet our re- quirements. Issues with evidence combination are discussed in Section 3.5, and our solution to these issues are explained in Section 3.6. A summary of the finalised set of evidence operations to support evidence based reasoning is presented in Section 3.8.

3.1

Introduction

The term evidence theory is used interchangeably in the literature with Dempster-Shafer theory. Dempster-Shafer theory concepts was originally in- troduced by Arthur Dempster [23, 22] and refined by Glen Shafer[95]in the 1970s. Since then, various extensions to Dempster-Shafer theory have ap- peared in the literature to cater for alternative scenarios. These are usually referred to under the term of Dempster-Shafer theory. An exception to this is Smet’s [100] transferable belief model (TBM), which is described further in section 3.4.3. We will use the term Dempster-Shafer theory to refer to evidence theory, but we will distinguish where we use aspects of Smet’s TBM.