The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

A cognitive architecture for emergency response

Plan recognition, cognitive workload estimation and human assistance have been extensively studied in the AI and human factors communities, resulting in many techniques being applied to domains of various levels of realism. These techniques have seldom been integrated and evaluated as complete systems. In this paper, we report on the development of an assistant agent architecture that integrates plan recognition, current and future user information needs, workload estimation and adaptive information presentation to aid an emergency response manager in making high quality decisions under time stress, while avoiding cognitive overload. We describe the main components of a full implementation of this architecture as well as a simulation developed to evaluate the system. Our evaluation consists of simulating various possible executions of the emergency response plans used in the real world and measuring the expected time taken by an unaided human user, as well as one that receives information assistance from our system. In the experimental condition of agent assistance, we also examine the effects of different error rates in the agent's estimation of user's stat or information needs

Anytime Cognition: An information agent for emergency response

Planning under pressure in time-constrained environments while relying on uncertain information is a challenging task. This is particularly true for planning the response during an ongoing disaster in a urban area, be that a natural one, or a deliberate attack on the civilian population. As the various activities pertaining to the emergency response need to be coordinated in response to multiple reports from the disaster site, a user finds itself cognitively overloaded. To address this issue, we designed the Anytime Cognition (ANTICO) concept to assist human users working in time-constrained environments by maintaining a manageable level of cognitive workload over time. Based on the ANTICO concept, we develop an agent framework for proactively managing a user’s changing information requirements by integrating information management techniques with probabilistic plan recognition. In this paper, we describe a prototype emergency response application in the context of a subset of the attacks devised by the American Department of Homeland Security

Speech-driven Animation with Meaningful Behaviors

Conversational agents (CAs) play an important role in human computer interaction. Creating believable movements for CAs is challenging, since the movements have to be meaningful and natural, reflecting the coupling between gestures and speech. Studies in the past have mainly relied on rule-based or data-driven approaches. Rule-based methods focus on creating meaningful behaviors conveying the underlying message, but the gestures cannot be easily synchronized with speech. Data-driven approaches, especially speech-driven models, can capture the relationship between speech and gestures. However, they create behaviors disregarding the meaning of the message. This study proposes to bridge the gap between these two approaches overcoming their limitations. The approach builds a dynamic Bayesian network (DBN), where a discrete variable is added to constrain the behaviors on the underlying constraint. The study implements and evaluates the approach with two constraints: discourse functions and prototypical behaviors. By constraining on the discourse functions (e.g., questions), the model learns the characteristic behaviors associated with a given discourse class learning the rules from the data. By constraining on prototypical behaviors (e.g., head nods), the approach can be embedded in a rule-based system as a behavior realizer creating trajectories that are timely synchronized with speech. The study proposes a DBN structure and a training approach that (1) models the cause-effect relationship between the constraint and the gestures, (2) initializes the state configuration models increasing the range of the generated behaviors, and (3) captures the differences in the behaviors across constraints by enforcing sparse transitions between shared and exclusive states per constraint. Objective and subjective evaluations demonstrate the benefits of the proposed approach over an unconstrained model.Comment: 13 pages, 12 figures, 5 table

Artificial Intelligence and Systems Theory: Applied to Cooperative Robots

This paper describes an approach to the design of a population of cooperative robots based on concepts borrowed from Systems Theory and Artificial Intelligence. The research has been developed under the SocRob project, carried out by the Intelligent Systems Laboratory at the Institute for Systems and Robotics - Instituto Superior Tecnico (ISR/IST) in Lisbon. The acronym of the project stands both for "Society of Robots" and "Soccer Robots", the case study where we are testing our population of robots. Designing soccer robots is a very challenging problem, where the robots must act not only to shoot a ball towards the goal, but also to detect and avoid static (walls, stopped robots) and dynamic (moving robots) obstacles. Furthermore, they must cooperate to defeat an opposing team. Our past and current research in soccer robotics includes cooperative sensor fusion for world modeling, object recognition and tracking, robot navigation, multi-robot distributed task planning and coordination, including cooperative reinforcement learning in cooperative and adversarial environments, and behavior-based architectures for real time task execution of cooperating robot teams