Automatic Personalization of the Human Computer Interaction Using Temperaments

In this paper we model a personalization system which takes into account the user interaction styles regarding a software artifact and the user temperament. We propose the use of Knowledge Intensive CBR where there are cases that represent specific variations of a given software artifact, and there are ontologies for the static knowledge. Our model is generic and reusable. In this paper we exemplify it with a system to personalize the Linux operating system environment. Introduction There are many efforts in research and industry to design friendly GUI’s. But what is the meaning of friendly? It is the same meaning for all kind of users? Obviously not. But how can the different perceptions of the users be determined? A solution may be the set of characteristics which users show when they interact with the computers. These characteristics are the result of the user’s temperament. The concept of Interaction Styles refers to all the ways the user can communicate or otherwise interact with the computer system. The concept belongs to the realm of Human Computer Interaction (HCI). In HCI textbooks, such as Shneiderman (Shneiderman 1997) and Preece et al. (Preece 1994), the types of interaction styles mentioned are usually in relation to the different computer interfaces: command language, form filling, menu selection, and direct manipulation. The Interaction Styles are influenced directly by the user’s temperament, there are studies that define these relations (Berens 2001). From these studies we can extract what may appeal as friendly and comfortable for users with a specific temperament. To be able to do this we needed to build a very flexible model where we can test many refinements in order to find out the adequate conditions for each temperament. We found the flexibility in the knowledge based systems, where the conditions were independent to the software. The model we are thinking about requires a lot of general knowledge to classify each feature and reason with it for each temperament. The knowledge intensive Case-Based Reasoning (KI-CBR) has these characteristics (Aamodt 1990; ∗Supported by the Spanish Committee of Science & Technology (TIN2005-09382-C02-01) Copyright c © 2006, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. Dı́az-Agudo & González-Calero 2000). This approach uses ontologies, which allows us to perform the mentioned tests with flexibility and reasoning about the topics that participate in the temperaments and in the possible variations of the system. These variations represent the different aspects of a certain system that are adequate to be personalized. The model we present is a personalization system independent of the specific software artifact. To apply it to a certain software, we only have to create the specific variations to populate the case base. In this article the domain of our example is to personalize the Linux operating system in general. To do so there is an ontology and a case base that describe the specific variations of Linux. In the next section we describe the three main components of our model: temperaments, variations and users. For each one of them we have formalized one ontology that is specialized in a case base. We then define how to use the mapping to define how a variation affects to a temperament. Then we describe the KI-CBR reasoning cycle and the architecture where we divide the main tasks in two sets: the Knowledge Engineer tasks and the user tasks. Before conclusions, there is a brief description of a domain case study where we are implementing a prototype of the model. The main ontologies and case bases This model relies heavily on ontologies. The static knowledge of each aspect of the design is described by terms in an ontology. Ontology is a term borrowed from philosophy that refers to the science of describing the kinds of entities in the world and how they are related. To formalize our ontologies we use the Ontology Web Language (OWL). An OWL ontology may include descriptions of classes, properties and their instances. Given such an ontology, the OWL formal semantics specifies how to derive its logical consequences, i.e. facts not literally present in the ontology, but entailed by the semantics. These entailments may be based on a single document or multiple distributed documents that have been combined using defined OWL mechanisms 1. The OWL reasoning capabilities relies on the Description Logics paradigm. We define cases as individuals of a concept which belongs to the ontology. A case has several slots and facets, reprehttp://www.w3.org/TR/owl-guide/