Modeling and Using Context

ISSN 2514-5711 - © ISTE Ltd

Journal issues

---

Recent articles

Introduction

The CONTEXT conference series has brought together researchers and practitioners from a wide range of disciplines and industries to present their work related to issues of context, contextual reasoning, and context-awareness to discuss commonalities and diversities in the different disciplinary approaches. It is unique in focusing on context as a subject of study in its own right, and it has become one of the top venues for context-related research.

---

Pragmatic Research on Context Modeling and Use

We argue for the centrality of a pragmatic approach to modeling and using context as a means of unifying research along all axes of context-related research: formal, cognitive, and pragmatic. After briefly discussing the evolution of the research on context in the past 25+ years, we describe the case for a focus on pragmatic research (e.g., applications) going forward. We then give three illustrative examples of pragmatic approaches leading to implementations.

---

Towards Measurable Efficient and Effective Metrics for Quality and Cost of Context

Despite the potential benefits of the context-driven intelligence delivered by Context Management Platforms (CMP), the lack of efficient and effective metrics for measuring Quality and Cost of Context (QoC and CoC) hinders them from uptake and commercialisation. Furthermore, the CMPs might have limited abilities to incorporate efficient QoC drivers and a suboptimal selection of QoC-aware context providers. This paper proposes QoC and CoC metrics and introduces a conceptual architecture to achieve the QoC and CoC awareness in CMPs, aiming to improve their efficiency and consumer experience.

---

The life-cycle of artificial contexts

Little has been published on connecting contexts as they are handled in natural systems and in computer science. This article selected some natural contexts and use them as an inspiration to highlight possible improvements on how contexts are designed and used in computer science. The authors found there are beneficial lessons and inspirations which have the potential to bring practical benefits as a result of this process.

---

Context-based Assistant Systems: First Results

The evolution of AI systems from expert systems, knowledge-based systems, joint cognitive systems, intelligent systems, intelligent assistant systems (IASs) and the coming generation of Context-based IASs (CIASs). CIASs require a deep focus on context and its relations with the users, the task at hand, the situation and the environment in which the task is accomplished by the user; the observation of users through their action and behaviors and not a profile library; a robust conceptual framework for modeling and managing context; and a computational tool for representing in a uniform way pieces of knowledge, of reasoning and of contexts.

---

Contextual Reasoning in Human Cognition and its Implications for Artificial Intelligence Systems

Research over the last 50 years has shown that reasoning with and about context is an essential aspect of human cognition, permeating language, memory, and reasoning capabilities. This integral process is developed over a lifetime through experiential learning. Given the goal of artificial intelligence to create human-like intelligence in a machine, it is essential to consider and incorporate the concept of context in intelligent system design and implementation. This paper discusses contextual reasoning in humans and how it helps humans successfully deal with complex cognitive tasks. We further link this research on context in cognitive psychology with the current research in contextual reasoning in Artificial Intelligence systems.