Teamwork requires mutual trust among team members. Establishing and maintaining trust depends upon alignment of mental models, an aspect of shared awareness. We present a theory of how maintenance of model alignment is integral to fluid changes in relative control authority (i.e., adaptive autonomy) in human-robot teamwork.

The agent is "always on," i.e. it is continuously available and aware (using a camera and infrared motion sensor) when the user is in its presence and can initiate interaction with the user, rather than requiring the user login to begin interaction. We expect that the agent will help reduce the user's isolation not just by always being around but also by specific activities that connect the user with friends, family and the local community. Our goal is for the agent to be a natural, humanlike presence that "resides" in the user's apartment. Beginning in the late summer of 2014, we will be placing our agents with users for a monthlong evaluation study. Figure 1: Virtual agent interface -- "Karen" Three issues of our project directly concern the topics of this workshop are: (1) the embodiment of the agent, (2) the engagement behaviors that are associated with being "always measures we will be using are questionnaires that assess the on," and (3) AI tools for support intelligent behavior.

Psychologists and social scientists have been researching creativity in humans for several years, and it has gained the attention of artificial intelligence and robotics researchers as well. In this abstract, we discuss the emotional and conversational interface required for a humanoid robot to socially interact with children in order to learn new creative concepts. We briefly describe the approach we are taking to develop such a humanoid robot that can collaborate with children to discover creative concepts.

In this paper we present a novel agent-based modeling methodology to predict rooftop solar adoptions in the residential energy market. We first applied several linear regression models to estimate missing variables for non-adopters, so that attributes of non-adopters and adopters could be used to train a logistic regression model. Then, we integrated the logistic regression model along with other predictive models into a multi-agent simulation platform and validated our models by comparing the forecast of aggregate adoptions in a typical zip code area with its ground truth. This result shows that the agent-based model can reliably predict future adoptions. Finally, based on the validated agent-based model, we compared the outcome of a hypothesized seeding policy with the original incentive plan, and investigated other alternative seeding policies which could lead to more adopters.

A paradigm of case-supported principle-based behavior (CPB) is proposed to help ensure ethical behavior of autonomous machines. We argue that ethically significant behavior of autonomous systems should be guided by explicit ethical principles determined through a consensus of ethicists. Such a consensus is likely to emerge in many areas in which autonomous systems are apt to be deployed and for the actions they are liable to undertake, as we are more likely to agree on how machines ought to treat us than on how human beings ought to treat one another. Given such a consensus, particular cases of ethical dilemmas where ethicists agree on the ethically relevant features and the right course of action can be used to help discover principles needed for ethical guidance of the behavior of autonomous systems. Such principles help ensure the ethical behavior of complex and dynamic systems and further serve as a basis for justification of their actions as well as a control abstraction for managing unanticipated behavior. The requirements, methods, implementation, and evaluation components of the CPB paradigm are detailed.

We present theoretical and empirical results demonstrating the usefulness of social choice functions in crowdsourcing for participatory democracies. First, we demonstrate the scalability of social choice functions by defining a natural notion of epsilon-approximation, and giving algorithms which efficiently elicit such approximations for two prominent social choice functions: the Borda rule and the Condorcet winner. This result circumvents previous prohibitive lower bounds and is surprisingly strong: even if the number of ideas is as large as the number of participants, each participant will only have to make a logarithmic number of comparisons, an exponential improvement over the linear number of comparisons previously needed. Second, we apply these ideas to Finland's recent off-road traffic law reform, an experiment on participatory democracy in real life. This allows us to verify the scaling predicted in our theory and show that the constant involved is also not large. In addition, by collecting data on the time that users take to complete rankings of varying sizes, we observe that eliciting partial rankings can further decrease elicitation time as compared to the common method of eliciting pairwise comparisons.

Welcome to the Second AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2014) held November 2-4, 2014, in Pittsburgh, Pennsylvania. This conference is an opportunity to build on the success of the First AAAI Human Computation and Crowdsourcing conference, and to promote the best scholarship in this vibrant and fast emerging, multidisciplinary area. The conference also comes on the heels of four HCOMP workshops, including two workshops hosted at the annual AAAI conference. The HCOMP conference is designed to be a venue for exchanging ideas and developments on principles, experiments, and implementations of systems that rely on programmatic access to human intellect to perform some aspect of computation, or where human perception, knowledge, reasoning, or coordinated activity contributes to the operation of larger systems and applications. Topics relevant to the discipline of human computation and crowdsourcing include human-computer interaction (HCI), computer-supported collaborative work (CSCW), cognitive psychology, organizational behavior, economics, information retrieval, databases, computer systems and programming languages, and optimization.

Games With a Purpose can enable an intelligent agent to persistently and pervasively sense the real world by using game players as reconfigurable sensors. We propose a technique whereby an intelligent agent incentivizes players to collect data by translating data collection tasks into a series of quests played on a mobile device. In this paper, we define the concept of Proactive Sensing and provide a framework for Game-Based Proactive Sensing that can adapt games and narrative that optimizes for data collection and long-term player engagement.

This paper studies a mechanism to incentivize agents who predict their own future actions and truthfully declare their predictions. In a crowdsouring setting (e.g., participatory sensing), obtaining an accurate prediction of the actions of workers/agents is valuable for a requester who is collecting real-world information from the crowd. If an agent predicts an external event that she cannot control herself (e.g., tomorrow's weather), any proper scoring rule can give an accurate incentive. In our problem setting, an agent needs to predict her own action (e.g., what time tomorrow she will take a photo of a specific place) that she can control to maximize her utility. Also, her (gross) utility can vary based on an eternal event. We first prove that a mechanism can satisfy our goal if and only if it utilizes a strictly proper scoring rule, assuming that an agent can find an optimal declaration that maximizes her expected utility. This declaration is self-fulfilling; if she acts to maximize her utility, the probabilistic distribution of her action matches her declaration, assuming her prediction about the external event is correct. Furthermore, we develop a heuristic algorithm that efficiently finds a semi-optimal declaration, and show that this declaration is still self-fulfilling. We also examine our heuristic algorithm's performance and describe how an agent acts when she faces an unexpected scenario.

Multi-agent Pathfinding is a relevant problem in a wide range of domains, for example in robotics and video games research. Formally, the problem considers a graph consisting of vertices and edges, and a set of agents occupying vertices. An agent can only move to an unoccupied, neighbouring vertex, and the problem of finding the minimal sequence of moves to transfer each agent from its start location to its destination is an NP-hard problem. We present Push and Rotate, a new algorithm that is complete for Multi-agent Pathfinding problems in which there are at least two empty vertices. Push and Rotate first divides the graph into subgraphs within which it is possible for agents to reach any position of the subgraph, and then uses the simple push, swap, and rotate operations to find a solution; a post-processing algorithm is also presented that eliminates redundant moves. Push and Rotate can be seen as extending Luna and Bekris's Push and Swap algorithm, which we showed to be incomplete in a previous publication. In our experiments we compare our approach with the Push and Swap, MAPP, and Bibox algorithms. The latter algorithm is restricted to a smaller class of instances as it requires biconnected graphs, but can nevertheless be considered state of the art due to its strong performance. Our experiments show that Push and Swap suffers from incompleteness, MAPP is generally not competitive with Push and Rotate, and Bibox is better than Push and Rotate on randomly generated biconnected instances, while Push and Rotate performs better on grids.