SA
S. Alaka
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Collaborative hybrid intelligence platform CHIP
A modular architecture for developing and testing personalized lifestyle support interactions
The rise of lifestyle-related, non-communicable diseases such as Type II diabetes, cardiovascular diseases, and depression has prompted the development of various behavior change technologies to promote sustained healthy behaviors. User adherence, however, has remained low. The Collaborative Hybrid Intelligence Platform CHIP is introduced to address adherence challenges by placing the user perspective at the center and facilitating dialogue-based interactions between users and their technical and non-technical support systems—including AI systems, clinicians and caretakers. These interactions aim to uncover barriers to adherence and collaboratively shape personalized lifestyle plans that align with a person’s preferences, values, and context. CHIP is a microservice-based research platform written in Python with modules implemented as Docker containers. Its modularity allows researchers to replace or adapt specific components, such as natural language reasoners, for technical evaluation and domain-specific adaptation.
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The rise of lifestyle-related, non-communicable diseases such as Type II diabetes, cardiovascular diseases, and depression has prompted the development of various behavior change technologies to promote sustained healthy behaviors. User adherence, however, has remained low. The Collaborative Hybrid Intelligence Platform CHIP is introduced to address adherence challenges by placing the user perspective at the center and facilitating dialogue-based interactions between users and their technical and non-technical support systems—including AI systems, clinicians and caretakers. These interactions aim to uncover barriers to adherence and collaboratively shape personalized lifestyle plans that align with a person’s preferences, values, and context. CHIP is a microservice-based research platform written in Python with modules implemented as Docker containers. Its modularity allows researchers to replace or adapt specific components, such as natural language reasoners, for technical evaluation and domain-specific adaptation.
Mixed-initiative generation of virtual worlds
A comparative study on the cognitive load of WFC and HSWFC
Procedural Content Generation (PCG) has long been proposed as an answer to the increased workload imposed on designers of virtual worlds, although often at the cost of controllability and expression of designer intent. Mixed-initiative approaches promise to overcome this, and valid proposals have been made of mixed-initiative editors, e.g. driven by the Wave Function Collapse (WFC) algorithm [12]. However, stock WFC operates on a simple tileset without any hierarchy or semantic clustering, preventing designers from fluently expressing level of detail, leaving the constraint solving burden partly on them instead of on the algorithm. Hierarchical Semantic Wave Function Collapse (HSWFC) claims to solve this problem by hierarchically structuring its tileset of semantic tiles, featuring meta-tiles that can undergo intermediate collapses [2]. Employing an HSWFC interactive editor offering such features may significantly reduce cognitive load and, thus, make such an editor more appealing for widespread use. We describe a user study to test this hypothesis, by comparing and discussing the cognitive load assessed on designers using either a stock WFC-driven editor or an HSWFC-driven editor. Our findings confirm that there is a significant reduction in cognitive load when the HSWFC editor is employed in comparison to the stock WFC editor.
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Procedural Content Generation (PCG) has long been proposed as an answer to the increased workload imposed on designers of virtual worlds, although often at the cost of controllability and expression of designer intent. Mixed-initiative approaches promise to overcome this, and valid proposals have been made of mixed-initiative editors, e.g. driven by the Wave Function Collapse (WFC) algorithm [12]. However, stock WFC operates on a simple tileset without any hierarchy or semantic clustering, preventing designers from fluently expressing level of detail, leaving the constraint solving burden partly on them instead of on the algorithm. Hierarchical Semantic Wave Function Collapse (HSWFC) claims to solve this problem by hierarchically structuring its tileset of semantic tiles, featuring meta-tiles that can undergo intermediate collapses [2]. Employing an HSWFC interactive editor offering such features may significantly reduce cognitive load and, thus, make such an editor more appealing for widespread use. We describe a user study to test this hypothesis, by comparing and discussing the cognitive load assessed on designers using either a stock WFC-driven editor or an HSWFC-driven editor. Our findings confirm that there is a significant reduction in cognitive load when the HSWFC editor is employed in comparison to the stock WFC editor.