Personal stories encoding health information are an effective tool for promoting health behavior change. As millions of stories about health are accumulated daily in blogs and in social networks online there is an opportunity to harvest and index a large database of health stories for interventions. We envision such a database increasing user education, engagement, motivation and rapport in our conversational agent-based health intervention systems. In this paper we propose a model of indexing health stories based on health behavior change theory, enhanced demographics and quality metrics.

Neuroprosthesis aims to supersede a damaged or degenerated brain caused by accidents or aging by an artificial brain that simulates and thereby restores the impaired brain functions. To replace the real brain, the artificial brain has to simulate the same functions of the real brain, and the simulation has to be conducted in realtime. We have built a large-scale spiking network model of the cerebellum that is composed of more than 100,000 neuron units and acts as a versatile supervised learning machine for spatiotemporal information. We implement it on a graphics processing unit (GPU) to conduct the numerical simulation in realtime owing to the parallel computing capability of GPUs. We propose to use the present model towards neuroprosthesis.

In many planning domains, it is impossible to construct plans that are guaranteed to keep the system completely safe. A common approach is to build probabilistic plans that are guaranteed to maintain system with a sufficiently high probability. For many such domains, bounds on system safety cannot be computed analytically, but instead rely on execution sampling coupled with a plan verification techniques. While probabilistic planning with verification can work well, it is not adequate in situations in which some modes of failure are very rare, simply because too many execution traces must be sampled (e.g., 1012) to ensure that the rare events of interest will occur even once. The P-CIRCA planner seeks to solve planning problems while probabilistically guaranteeing safety. Our domains frequently involve verifying that the probability of failure is below a low threshold (< 0.01). Because the events we sample have such low probabilities, we use Importance sampling (IS) (Hammersley and Handscomb 1964; Clarke and Zuliani 2011) to reduce the number of samples required. However, since we deal with an abstracted model, we cannot bias all paths individually. This prevents IS from achieving a correct bias. To compensate for this drawback we present a concept of DAGification to partially expand our representation and achieve a better bias.

Our work aims to support decision making in situations where the source of the information on which decisions are based is of varying trustworthiness. Our approach uses formal argumentation to capture the relationships between such information sources and conclusions drawn from them. This allows the decision maker to explore how information from particular sources impacts the decisions they have to make. We describe the formal system that underlies our work, and a prototype implementation of that system, applied to a problem from military decision making.

Future robotic systems are expected to transition from tools to teammates , characterized by increasingly autonomous, intelligent robots interacting with humans in a more naturalistic manner, approaching a relationship more akin to human–human teamwork. Given the impact of trust observed in other systems, trust in the robot team member will likely be critical to effective and safe performance. Our thesis for this paper is that trust in a robot team member must be appropriately calibrated rather than simply maximized.  We describe how the human team member’s understanding of the system contributes to trust in human-robot teaming, by evoking mental model theory. We discuss how mental models are related to physical and behavioral characteristics of the robot, on the one hand, and affective and behavioral outcomes, such as trust and system use/disuse/misuse, on the other.  We expand upon our discussion by providing recommendations for best practices in human-robot team research and design and other systems using artificial intelligence.

We describe research towards creating a computational model for recognizing interpersonal trust in social interactions. We found that four negative gestural cues— leaning-backward, face-touching, hand-touching, and crossing-arms—are together predictive of lower levels of trust. Three positive gestural cues—leaning- forward, having arms-in-lap, and open-arms—are predictive of higher levels of trust. We train a probabilistic graphical model using natural social interaction data, a “Trust Hidden Markov Model” that incorporates the occurrence of these seven important gestures throughout the social interaction. This Trust HMM predicts with 69.44% accuracy whether an individual is willing to behave cooperatively or uncooperatively with their novel partner; in comparison, a gesture-ignorant model achieves 63.89% accuracy. We attempt to automate this recognition process by detecting those trust-related behaviors through 3D motion capture technology and gesture recognition algorithms. We aim to eventually create a hierarchical system—with low-level gesture recognition for high-level trust recognition—that is capable of predicting whether an individual finds another to be a trustworthy or untrustworthy partner through their non- verbal expressions.

The paper discusses the issue of trusting, or the active management of trust (Fitzhugh/etal:2011), in human-robot teams. The paper approaches the issue from the viewpoint of asymmetric agency, and social sentience. The assumption is that humans and robots experience reality differently (asymmetry), and that a robot is endowed with an explicit (deliberative) awareness of its role within the team, and of the social dynamics of the team (social sentience). A formal approach is outlined, to provide the basis for a model of trusting in terms of (i) trust in information and how to act upon that (as judgements about actions and interactions, at the task-level), and (ii) the reflection of trust between actors in a team, in how social dynamics get directed over time (team-level). The focus is thus primarily on the integration of trust and its adaptation in the dynamics of collaboration.

There is a recognized need to employ autonomous agents in domains that are not amenable to conventional automation and/or which humans find difficult, dangerous, or undesirable to perform. These include time-critical and mission-critical applications in health, defense, transportation, and industry, where the consequences of failure can be catastrophic. A prerequisite for such applications is the establishment of well-calibrated trust in autonomous agents. Our focus is specifically on human-machine trust in deployment and operations of autonomous agents, whether they are embodied in cyber-physical systems, robots, or exist only in the cyber-realm. The overall aim of our research is to investigate methods for autonomous agents to foster, manage, and maintain an appropriate trust relationship with human partners when engaged in joint, mutually interdependent activities. Our approach is grounded in a systems-level view of humans and autonomous agents as components in (one or more) encompassing meta-cognitive systems. Given human predisposition for social interaction, we look to the multi-disciplinary body of research on human interpersonal trust as a basis from which we specify engineering requirements for the interface between human and autonomous agents. If we make good progress in reverse engineering this "human social interface," it will be a significant step towards devising the algorithms and tests necessary for trustworthy and trustable autonomous agents. This paper introduces our program of research and reports on recent progress.

This symposium will explore the various meaning aspects and meanings of trust between humans and machines in various situational contexts, and the social dynamics of trust in teams or organizations composed of autonomous machines working together with humans. We will seek to identify and/or develop methods for engendering trust between humans and autonomous machines, to consider the static and dynamic aspects of trust, and to propose metrics for measuring trust.

In this paper first I introduce curation and affordance in chance discovery. According to Matsumura's definition, a shikake is a trigger to start a certain action or to change person's mind and behaviour. As a result of the action, all or part of problem will be solved. A chance and shikake are in a certain sense similar. In addition, affrodance seems to play a significant role in shikakeology. From the point I will discuss the relationships between chance discovery and Shikakeology.