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The authors introduce a new method for the establishment of an extension to current product models in order to enhanced communication between human and modeling processes. The main contribution of the reported work to product modeling is a dialogue input and model representation of human intent in order to make assistance for human who applies product model and needs important details from thinking...
In a previous work we presented an approach to allow invocation of services whose interface and behavior differ from each other. Such approach requires the definition of scripts by some human being able to completely understand the mismatches and properly combine the mapping functions. Furthermore, such approach requires an intensive effort from a system integrator that, in the worst case in which...
Adaptive software is a closed-loop system which aims at adjusting itself in different situations at runtime. This paper looks at adaptation as changes in the context of dynamic software evolution, and proposes a conceptual model for these changes based on Activity Theory. This model consists of a hierarchy of activities making changes, and the objectives motivating these changes. This model is an...
The paper presents the main findings of the ELeGI project, namely its learning model and software architecture to support the creation and execution of complex learning processes.The learning model defined in ELeGI promotes and supports a learning paradigm centred on knowledge construction using experiential based and collaborative learning approaches in a contextualised, personalised and ubiquitous...
This paper presents a new computational model where both dasiathe results of emotional interactions from the intrinsic emotion and recognitionpsila and dasiathe extrinsic environmental stimulation (another agent)psila, the two elements play an integral role in everyday life. We take states from a set of basic emotions-happiness, surprise, anger, fear, sadness, disgust, and updates its state depending...
Current approaches to adaptive game AI require either a high quality of utilised domain knowledge, or a large number of adaptation trials. These requirements hamper the goal of rapidly adapting game AI to changing circumstances. In an alternative, novel approach, domain knowledge is gathered automatically by the game AI, and is immediately (i.e., without trials and without resource-intensive learning)...
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