Mobile Intention Recognition

Mobile Intention Recognition addresses problems of practical relevance for mobile system engineers: how can we make mobile assistance systems more intelligent? How can we model and recognize patterns of human behavior which span more than a limited spatial context? This text provides an overview on plan and intention recognition, ranging from the late 1970s to very recent approaches. This overview is unique as it discusses approaches with respect to the specificities of mobile intention recognition. This book covers problems from research on mobile assistance systems using methods from artificial intelligence and natural language processing. It thus addresses an extraordinary interdisciplinary audience.

This original and timely monograph describes a unique self-contained excursion that reveals to the readers the roles of two basic cognitive abilities, i.e. intention recognition and arranging commitments, in the evolution of cooperative behavior. This book analyses intention recognition, an important ability that helps agents predict others’ behavior, in its artificial intelligence and evolutionary computational modeling aspects, and proposes a novel intention recognition method. Furthermore, the book presents a new framework for intention-based decision making and illustrates several ways in which an ability to recognize intentions of others can enhance a decision making process. By employing the new intention recognition method and the tools of evolutionary game theory, this book introduces computational models demonstrating that intention recognition promotes the emergence of cooperation within populations of self-regarding agents. Finally, the book describes how commitment provides a pathway to the evolution of cooperative behavior, and how it further empowers intention recognition, thereby leading to a combined improved strategy.

This book constitutes the refereed proceedings of the International Conference on Spatial Cognition, Spatial Cognition 2008, held in Freiburg, Germany, in September 2008. The 27 revised full papers presented together with 3 invited lectures were carefully reviewed and selected from 54 submissions. The papers are organized in topical sections on spatial orientation, spatial navigation, spatial learning, maps and modalities, spatial communication, spatial language, similarity and abstraction, concepts and reference frames, as well as spatial modeling and spatial reasoning.

This SpringerBrief provides a synergistic overview of technology trends by emphasizing five linked perspectives: crowd+cloud machines, extreme cooperation with smart things, scalable context-awareness, drone services for mobile crowds and social links in mobile crowds. The authors also highlight issues and challenges at the intersection of these trends. Topics covered include cloud computing, Internet of Things, mobile and wearable computing, crowd computing, the culture of thing sharing, collective computing, and swarm dynamics. The brief is a useful resource and a starting point for researchers, students or anyone interested in the contemporary computing landscape.

This book is concerned with behaviour monitoring and interpretation with regard to two main areas of focus: the investigation of motion patterns and ambient assisted living. It presents state-of-the-art contributions on research in both these areas. The first section consists of chapters discussing recent developments in monitoring and representing behaviours, with a particular focus on movement-based behaviour. It includes: methods for monitoring and analysing pedestrian motion behaviours; typical motion patterns of single people and groups of people. In particular, a number of qualitative spatial representations are presented for describing patterns topologically and ordinally. The next part of the volume is more application-driven. Several case studies present the monitoring and support of people with cognitive impairments in smart environments, showing in particular how AI techniques are applied in these contexts and also how ambient assisted physical activity systems help to increase the engagement of seniors in physical activities. Investigations to show how monitored behaviours can be interpreted in smart environments are then described: a survey on knowledge-intensive methods for intention recognition; the detection of high-level daily activities by analysis of team behaviours in smart environments and a model for how ambient intelligence systems can automatically discover patterns of user behaviours. Finally, the publication discusses the infrastructure of smart environments.

The two volumes LNAI 11649 and LNAI 11650 constitute the refereed proceedings of the 20th Annual Conference "Towards Autonomous Robotics", TAROS 2019, held in London, UK, in July 2019. The 74 full papers and 12 short papers presented were carefully reviewed and selected from 101 submissions. The papers present and discuss significant findings and advances in autonomous robotics research and applications. They are organized in the following topical sections: robotic grippers and manipulation; soft robotics, sensing and mobile robots; robotic learning, mapping and planning; human-robot interaction; and robotic systems and applications.

This book consists of a number of chapters addressing different aspects of activity recognition, roughly in three main categories of topics. The first topic will be focused on activity modeling, representation and reasoning using mathematical models, knowledge representation formalisms and AI techniques. The second topic will concentrate on activity recognition methods and algorithms. Apart from traditional methods based on data mining and machine learning, we are particularly interested in novel approaches, such as the ontology-based approach, that facilitate data integration, sharing and automatic/automated processing. In the third topic we intend to cover novel architectures and frameworks for activity recognition, which are scalable and applicable to large scale distributed dynamic environments. In addition, this topic will also include the underpinning technological infrastructure, i.e. tools and APIs, that supports function/capability sharing and reuse, and rapid development and deployment of technological solutions. The fourth category of topic will be dedicated to representative applications of activity recognition in intelligent environments, which address the life cycle of activity recognition and their use for novel functions of the end-user systems with comprehensive implementation, prototyping and evaluation. This will include a wide range of application scenarios, such as smart homes, intelligent conference venues and cars.

This thesis is concerned with intention recognition for a humanoid robot and investigates how the challenges of uncertain and incomplete observations, a high degree of detail of the used models, and real-time inference may be addressed by modeling the human rationale as hybrid, dynamic Bayesian networks and performing inference with these models. The key focus lies on the automatic identification of the employed nonlinear stochastic dependencies and the situation-specific inference.