D2.1 Requirements and Specification - CORBYS

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D2.1 Requirements and Specification dialogue system which fuses speech, facial expression and gesture for “both input and output via an anthropomorphic and affective user interface”, as an Embodied Conversational Agent (ECA) or Avatar (Corradini et al. 2005). In all such systems, input events are assigned a weight or “confidence score” that is considered during high level fusion for creating a set of situation interpretations that are ranked by their combined score (Corradini et al. 2005). 11.3 Relevant approaches We review different approaches and techniques related to situation assessment for solving the problem of identification of objects, scenarios, situations of interest in an operational environment. 11.3.1 Rulebased Expert Systems These are knowledge based systems consisting of a rule base and a fact base. Rules encode domain knowledge whereas facts represent the state of the environment. The outcome of a rule is deduced by a preceding condition part of the rule using logic, where facts act as the input to the condition. When rules are executed, new facts corresponding to the conclusion are added to the fact base. The reasoning of the rulebased systems is controlled by an inference engine. To achieve situation awareness, this type of system can be used with ontological mappings of the environment in terms of the objects, relations, situations etc. within this environment. While such systems are easily deployed and various expert tools are available that require only the rule definitions to be specified by the user, the major drawback of these systems is that they are largely deterministic in nature. Therefore, a lack of provision for uncertainty values for rules introduces limitations and imposes demands on the user regarding careful consideration in developing the rules (Russell and Norvig, 2003). Rule-based systems also lack the possibility to perform temporal reasoning. Learning new rules in an automatic fashion is not so straight forward either. This necessitates manual human input which can be a tedious task. 11.3.2 Fuzzy Logic Fuzzy logic deals with reasoning that is approximate rather than exact. Fuzzy logic variables can have truth value ranging between 0 and 1, as opposed to two-valued logic (true or false), hence allowing handling of partial truth where truth value can range between completely true and completely false (Novak et al. 1999). In classical set theory, elements are introduced to a set in binary terms, overseen by a hard condition, whereby an element either belongs to or does not belong to the set. In fuzzy set theory, elements can be assessed gradually in terms of their candidature using a membership function that defines the degree to which an element can be considered to be inside or outside of the fuzzy set. The use of fuzzy logic allows for complex real world modelling. Data fuzzification allows a transition from numerical to fuzzy format, followed by an evaluation or fuzzy inferencing of the fuzzy conditions. The output is then de-fuzzified i.e. transited back to numerical format. There exist several fuzzy inference engines such as Java based Jess (Orchard, 2001) and Prolog based Flint (Shalfield, 2005) among others. 11.3.3 Casebased Reasoning Case Based Reasoning (CBR) is a method used in computing to allow systems to solve problems by recalling how similar problems were dealt with previously. The system is given a set of basic cases and a set of rules by which it can alter these cases. When given a problem, the system finds the most relevant case (or multiple cases) and if required, modifies it to solve the problem. The basic premise for CBR is that similar cases can be satisfied by similar solutions. Hence, if a similar case to the received input case is found present in the CBR repository, the solution can be adapted to the current case and the problem can be solved. If the modified solution is satisfactory, the new solution is retained in the repository together with the problem. 116
D2.1 Requirements and Specification Problem 4 Retention Solution 1 Retrieval Figure 1: CBR Process Case Based Repository 1. The problem is entered into the system and analysed. 2. The case which closest matches the details of the problem is selected. 3. This case is modified to better fit the problem using predefined rules. This becomes the solution which is presented. 4. The solution is analysed for its effect and is stored for future use along with an indication of its successfulness. This allows the system to learn. The system should be able to learn not only from its successful solutions, but also from its failed solutions (success driven and failure driven learning). Successful solutions can be stored so that the solution can be reused without regenerating it. Solutions which failed to solve the problem can be used to generate better solutions for use at a later stage. CBR can be used to identify situations presented as cases from a repository of known situations; however, the mapping of a situation as a case and subsequent measurement of similarity between two cases poses a problem. Furthermore, the existence of an associated solution becomes irrelevant as soon as the situation is matched to a template or a case from the repository. 11.3.4 Bayesian Networks 2 Selection Bayesian networks are probabilistic graphical models that represent a set of random variables and their conditional dependencies via directed acyclic graphs. Bayesian networks are commonly used for probabilistic reasoning in the context of situation assessment (Das et al. 2002; Bladon et al. 2002; Higgins 2005). Bayesian networks are based on Bayes theorem which computes posterior or inverse probability for a proposition i.e., given the prior or unconditional probabilities of A and B, and knowing the conditional probability of B given A, what is the conditional probability of A given B? Bayesian nodes in the networks represent propositions, random variables, unknown parameters or hypotheses. Nodes are connected by edges, that represent conditional dependency, and unconnected nodes therefore represent variables that are conditionally independent. Each node has an associated probability function that gives the variable probability represented by the node, upon receiving a set of values as input. Priori probabilities and conditional probabilities have to be specified for each node in the network. Inverse probabilities for each node can then be computed using Bayes rule as new input is received into the network. In the context of situation assessment, Das et al. (2002) lists two important points that must be observed when 117 Application of rules to modify selected cases Figure 27: Case-Based Reasoning process 3 Presentation
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