Diagnosis of a clinical condition is a challenging task, which often requires significant medical investigation. Previous work related to diagnostic inferencing problems mostly consider multivariate observational data (e.g. physiological signals, lab tests etc.). In contrast, we explore the problem using free-text medical notes recorded in an electronic health record (EHR). Complex tasks like these can benefit from structured knowledge bases, but those are not scalable. We instead exploit raw text from Wikipedia as a knowledge source. Memory networks have been demonstrated to be effective in tasks which require comprehension of free-form text. They use the final iteration of the learned representation to predict probable classes. We introduce condensed memory neural networks (C-MemNNs), a novel model with iterative condensation of memory representations that preserves the hierarchy of features in the memory. Experiments on the MIMIC-III dataset show that the proposed model outperforms other variants of memory networks to predict the most probable diagnoses given a complex clinical scenario.

The capability of collaboration is critical in the design of symbiotic cognitive systems. To obtain this functional capability, a cognitive system should possess evaluative and communicative processes. Emotions and their underlying processes provide such functions in social and collaborative environments. We investigate the mutual influence of affective and collaboration processes in a cognitive theory to support the interaction between humans and robots or virtual agents. We have developed new algorithms for these processes, as well as a new overall computational model for implementing collaborative robots and agents. We build primarily on the cognitive appraisal theory of emotions and the SharedPlans theory of collaboration to investigate the structure, fundamental processes and functions of emotions in a collaboration context.

We present a system that integrates robot task execution with user input and feedback at multiple abstraction levels in order to achieve greater adaptability in new environments. The user can specify a hierarchical task, with the system interactively proposing logical action groupings within the task. During execution, if tasks fail because objects specified in the initial task description are not found in the environment, the robot proposes substitutions autonomously in order to repair the plan and resume execution. The user can assist the robot by reviewing substitutions. Finally, the user can train the robot to recognize and manipulate novel objects, either during training or during execution. In addition to this single-user scenario, we propose extensions that leverage crowdsourced input to reduce the need for direct user feedback.

The AAAI 2014 Fall Symposium Series was held Thursday through Saturday, November 13–15, at the Westin Arlington Gateway in Arlington, Virginia adjacent to Washington, DC. The titles of the seven symposia were Artificial Intelligence for Human-Robot Interaction, Energy Market Prediction, Expanding the Boundaries of Health Informatics Using AI, Knowledge, Skill, and Behavior Transfer in Autonomous Robots, Modeling Changing Perspectives: Reconceptualizing Sensorimotor Experiences, Natural Language Access to Big Data, and The Nature of Humans and Machines: A Multidisciplinary Discourse. The highlights of each symposium are presented in this report.

The program also included six keynote presentations, a funding panel, a community panel, and multiple breakout sessions. The keynote presentations, given by speakers that have been working on AI for HRI for many years, focused on the larger intellectual picture of this subfield. Each speaker was asked to address, from his or her personal perspective, why HRI is an AI problem and how AI research can bring us closer to the reality of humans interacting with robots on everyday tasks. Speakers included Rodney Brooks (Rethink Robotics), Manuela Veloso (Carnegie Mellon University), Michael Goodrich (Brigham Young University), Benjamin Kuipers (University of Michigan), Maja Mataric (University of Southern California), and Brian Scassellati (Yale University).

A number of effective techniques for constraint-based optimization can be used to generate either diverse or high-quality solutions independently, but no framework is devoted to accomplish both simultaneously. In this paper, we tackle this issue with a generic paradigm that can be implemented in most existing solvers. We show that our technique can be specialized to produce diverse solutions of high quality in the context of over-constrained problems. Furthermore, our paradigm allows us to consider diversity from a different point of view, based on generic concepts expressed by global constraints.

Analogies are a fundamental human reasoning pattern that relies on relational similarity. Understanding how analogies are formed facilitates the transfer of knowledge between contexts. The approach presented in this work focuses on obtaining precise interpretations of analogies. We leverage noisy semantic networks to answer and explain a wide spectrum of analogy questions. The core of our contribution, the Semantic Similarity Engine, consists of methods for extracting and comparing graph-contexts that reveal the relational parallelism that analogies are based on, while mitigating uncertainty in the semantic network. We demonstrate these methods in two tasks: answering multiple choice analogy questions and generating human readable analogy explanations. We evaluate our approach on two datasets totaling 600 analogy questions. Our results show reliable performance and low false-positive rate in question answering; human evaluators agreed with 96% of our analogy explanations.

In this work we present a method that allows to learn a cost function for motion planning of human-robot collaborative manipulation tasks where the human and the robot manipulate objects simultaneously in close proximity. Our approach is based on inverse optimal control which enables, considering a set of demonstrations, to find a cost function balancing different features. The cost function that is recovered from the human demonstrations is composed of elementary features, which are designed to encode notions such as safely, legibility and efficiency of the manipulation motions. We demonstrate the approach on data gathered from motion capture of human-human manipulation in close proximity of blocks on a table. To demonstrate the feasibility and efficacy of our approach we provide initial test results consisting of learning a cost function and then planning for the human kinematic model used in the learning phase.

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.

In this work, we focus on advancing the state of the art in intelligent agents that can learn complex procedural tasks from humans. Our main innovation is to view the interaction between the human and the robot as a mixed- initiative collaboration. Our contribution is to integrate hierarchical task networks and collaborative discourse theory into the learning from demonstration paradigm to enable robots to learn complex tasks in collaboration with the human teacher.