Traditional social science research has been unable to satisfactorily aggregate individual level data to group, organization and systems levels, making it one of social science’s biggest challenges (Giles, 2011). For game and social theory, we believe that the fault can be attributed to the lack of valid distance measures (e.g., the arbitrary ordering of cooperation and competition precludes a Hilbert space distance metric for the ordering of and gradations between these social behaviors, making game theory normative). Alternatively, we offer a theory of social interdependence with countable mathematics based on bistable or multi-stable perspectives and linear algebra. The evidence that is available is supportive. It indicates that meaning is a one-sided, stable, classical interpretation, not only making the correspondence between beliefs and objective reality in social settings incomplete, raising questioning about static theories from earlier eras (i.e., Axelrod’s evolution of cooperation; Simon’s bounded rationality). The result indicates for open systems (democracies) that interpretations evolve naturally to become orthogonal (Nash equilibria), that orthogonal interpretations generate the information to drive social evolution, but that in closed systems (dictatorships), dependent on the enforcement of social cooperation and the suppression of opposing points of view, evolution slows or stops (e.g., China, Iran or Cuba), causing capital and energy to be wasted, misdirected or misallocated as leaders suppress the interpretations that they alone have the authority to label as unethical, immoral, or irreligious. We conclude that a mathematics based on NE is feasible.

Service composition, which achieves the goal of value-added services, has been considered as the core technique of Service-oriented Computing (SOC). To cope with the challenge of ever-increasing number of web services, graph-based web service network has emerged as a potential solution to the state of art SOC. In such a way, composite services are constructed by applying searching algorithms to the built graph, and proved to achieve outstanding performance in complexity. However, web service network suffers two crucial disadvantages: poor connectivity and negative links, and both of them have crucial negative impact on service composition. To cope with the problems, we propose two methods in this paper. Firstly, leveraging social network analysis, we focus on enriching web service network by adding valuable services, which will play positive roles in solving poor connective problem. Secondly, we show a serious status that numerous negative links contained in the underlying networks, and then we propose to identify and remove the negative links based on users’ historical composite services.

This paper focuses on care plans (i.e., rough schedules) in care houses and evaluates them from the viewpoint of a deep and stable sleep which contributes to provide comfortable and healthy life for aged persons. For this purpose, this paper investigates the care plans which are basically based on the current care plans but change a small part of a schedule as an aged person desires. Through the human subject experiments in the actual care house, the following implications have been revealed: (1) the proposed care plan decreases the time of the light sleep; and (2) the proposed care plan provides the deep sleep (i.e., 9 years younger sleep in our experiment).

One of the original applications of crowdsourcing the construction of knowledge is Wikipedia, which relies entirely on people to contribute, extend, and modify the representation of knowledge. This paper presents a case for combining AI and wisdom of the crowds for the social construction of knowledge. Our social-computational approach to collective intelligence combines the strengths of human cognitive diversity in producing content and the capabilities of an AI, through methods such as topic modeling, to link and synthesize across these human contributions. In addition to drawing from established domains such as Wikipedia for inspiration and guidance, we present the design of a system that incorporates AI into wisdom of the crowds to develop a knowledge base on sustainability. In this setting the AI plays the role of scholar, as might many of the other participants, drawing connections and synthesizing across contributions. We close with a general discussion, speculating on educational implications and other roles that an AI can play within an otherwise collective human intelligence.

A need for sleep monitoring is increasing in modern society. However, sleep stage scoring is time consuming, and large inconsistencies may exist among scorers. The settings for the recordings are also complicated and usually need to be professionally prepared. If simple small equipment could record human EEG and detect sleep stages, it would bring significant benefits to a large population. We thus developed a simple frequency-based sleep stage classifier by single EEG derivation, and evaluated the performance of the classifier. It showed a potential to work as well as the other known automated classifiers. The classifier was not based on specific frequency bands or EEG patterns. It could perform as well with poor quality signals and could easily be adopted to score any other biological signals.

This paper proposes a novel telemedicine system for type 2 diabetes patients. The proposed system supports the patient self-management via a set of telemedicine devices, consisting of health sensors and a smart phone. The proposed system covers not only the sensor data but also the diet (food) and exercise data. To capture the food information, we also developed the voice recognition module focusing on the food names. The basic feasibility of the system is practically demonstrated in the preliminary experiment.

Constructing robust controllers to perform tasks in large, continually changing worlds is a difficult problem. A long-lived agent placed in such a world could be required to perform a variety of different tasks. For this to be possible, the agent needs to be able to abstract its experiences in a reusable way. This paper addresses the problem of online multitask decision making in such complex worlds, with inherent incompleteness in models of change. A fully general version of this problem is intractable but many interesting domains are rendered manageable by the fact that all instances of tasks may be described using a finite set of qualitatively meaningful contexts. We suggest an approach to solving the multitask problem through decomposing the domain into a set of capabilities based on these local contexts. Capabilities resemble the options of hierarchical reinforcement learning, but provide robust behaviours capable of achieving some subgoal with the associated guarantee of achieving at least a particular aspiration level of performance. This enables using these policies within a planning framework, and they become a level of abstraction which factorises an otherwise large domain into task-independent sub-problems, with well-defined interfaces between the perception, control and planning problems. This is demonstrated in a stochastic navigation example, where an agent reaches different goals in different world instances without relearning.

Existing personalisation approaches, such as collaborative filtering or content based recommendations, are highly dependent on the domain and/or the source of the data. Therefore, there is a need for more accurate means to capture and model the interests of the user across domains, and to interlink them in a semantically-enhanced interest graph. We propose a new approach for multi-source, cross-genre recommendations that can exploit the heterogeneous nature of user profile data, which has been aggregated from multiple personalised web services, such as blogs, wikis and microblogs. Our approach is based on the Spreading Activation model that exploits intrinsic links between entities across a number of data sources. The proposed method is highly customizable and applicable both to generic and specific recommendation scenarios and use cases. With the growing number of Social Web applications in the enterprise (blogs, wikis, micro blogging, etc.), it becomes difficult for knowledge workers to avoid content overload and to quickly identify relevant people, communities and information. We demonstrate the application of our approach in an industrial use case that involves recommendation of social semantic data across multiple services in a distributed collaborative environment.

Self-tracking technologies used by healthy self-experimenters and chronic illness patients are relatively new but offer potential to accelerate the discovery of evidence-based protocols in the fields of human biology and medicine. Among both academic researchers and real-world practitioners in these fields there is an ever-present body of misinformation, leading to the proliferation of myth-based protocols in health-promoting lifestyles and treatment. This collection of four case studies spanning seven years’ worth of observations in a self-experimenting endurance athlete and, later, chronically ill individual, aims to bring to attention themost common incorrect assumptions regarding: nutrition, athletic performance, sleep, and treatment of hypothyroidism. We hope that, with these insights about misleading scientific conclusions, artificial intelligence researchers and anyone interested in developing technological solutions for public health purposes, will explore ways to bridge the gap between academic research and real-world practice of optimizing human biology, and rid the misinformation on bothsides.

Nexus Network Learner is an intelligent agent based simulation used to study Irregular Warfare (IW) in several major studies at the Department of Defense (DoD). Heterogeneous autonomous agents, each with their own separated inductive learning mechanism, have initial attributes and behaviors in proportion to demographic groups in the simulated population, and learn new behaviors as they serve culturally based goals. Nexus agents create a dynamic role-based network, and learn how to choose partners as well as what behaviors they should have with their network partners. As Nexus agents coevolve, nexus models the emergence of social institutions from individual behaviors, the fundamental social aggregation challenge. Nexus models the formation of learned vicious and virtuous cycles of behavior, some of which have higher average utility for the agents than others, and can be used to test the effects of interventions on the natural motivation-based system. An experiment is presented that uses Nexus to model the vicious cycle of corruption in an African country, from the first Irregular Warfare Analytical baseline at the Office of the Secretary of Defense (Messer 2009).