By Matthias Bezold
Adaptive Multimodal Interactive Systems introduces a common framework for adapting multimodal interactive platforms and contains a close dialogue of every of the stairs required for variation. This ebook additionally investigates how interactive platforms could be stronger when it comes to usability and person friendliness whereas describing the exhaustive person exams hired to guage the awarded approaches.
After introducing basic thought, a wide-spread strategy for person modeling in interactive platforms is gifted, starting from an statement of uncomplicated occasions to an outline of higher-level person habit. variations are awarded as a collection of styles just like these identified from software program or usability engineering.These styles describe habitual difficulties and current confirmed recommendations. The authors contain a dialogue on whilst and the way to hire styles and supply assistance to the process fashion designer who desires to upload adaptivity to interactive platforms. as well as those styles, the booklet introduces an model framework, which shows an abstraction layer utilizing Semantic net technology.Adaptations are carried out on most sensible of this abstraction layer by means of making a semantic illustration of the difference styles. The styles hide either graphical interfaces in addition to speech-based and multimodal interactive systems.
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Additional info for Adaptive Multimodal Interactive Systems
It employs an architecture called Jaspis. The system consists of managers, agents, and evaluators. Managers take care of specific components, for instance a dialog manager or a presentation manager. Agents handle (possibly very specific) situations, such as speech recognition errors. Different agents implement different strategies for the same situation and evaluators select among the available agents. The advantages of the agent-based approach are a high degree of flexibility and the possibility to implement different strategies and select among them at runtime.
The individual observations that are used to infer the user’s goal are uncertain. g. when certain observations are part of different plans. Moreover, users tend to work on different tasks in parallel. Third, plan recognition algorithms have to scale up in large domains with plan libraries that contain numerous plans a user possibly works on. In this work, we introduce an approach for adaptive interactive systems that employs a technique called task modeling (Patern`o, 2001). It describes the user-system interaction by means of tasks, which define possible user actions without speculation about the user’s intentions.
These algorithms may be implemented within the user modeling framework presented in this work. The selection of the algorithms depends on the requirements of a specific interactive system. We present a number of user modeling algorithms that we developed or adapted for adaptive interactive systems in Chapter 3. 2 Plan Recognition and Task Models Plan recognition is the process of observing user actions and determining the goal a user tries to accomplish. A plan represents the order of actions required to reach that goal.