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Cognitive architectures are computational theories of reasoning the human mind engages in as it processes facts and experiences. A cognitive architecture uses declarative and procedural knowledge to represent mental constructs that are involved in decision making. Employing a model of behavioral and perceptual constraints derived from a set of one or more scenarios, the architecture reasons about the most likely consequence(s) of a sequence of events. Reasoning of any complexity and depth involving computational processes, however, is often opaque and challenging to comprehend. Arguably, for decision makers who may need to evaluate or question the results of autonomous reasoning, it would be useful to be able to inspect the steps involved in an interactive, graphical format. When a chain of evidence and constraint-based decision points can be visualized, it becomes easier to explore both how and why a scenario of interest will likely unfold in a particular way., in SPIE Visualization and Data Analysis, Burlingame, CA, 4-6 Feb 2013.

A comparative cognitive model of two manipulations of a complex dual task in which 3D audio cueing was used to improve operator performance is presented. The model is implemented within the EPIC cognitive architecture and describes extensions that were necessary to simulate gaze shifts and the allocation of attention between separated task displays. A simulation of meta-cognitive decision-making to explain unprompted, volitional shifts of attention and the effect of audio cueing on performance and the frequency of attention shifts are explored., Proceedings of the Annual Meeting of the Cognitive Science Society (28th), Vancouver, BC, July 26-29, 2006. Presented as a poster. Published as a six-page paper, 1044-1049. The original document contains color images.

How do children learn how to play hide and seek? At ages 3-4, children do not typically have perspective taking ability, so their hiding ability should be extremely limited. The authors show through a case study that a 3-1/2-year-old child can, in fact, play a credible game of hide and seek, even though she does not seem to have perspective taking ability. They propose that children are able to learn how to play hide and seek by learning the features and relations of objects (e.g., containment, under) and use that information to play a credible game of hide and seek. They model this hypothesis within the ACT-R cognitive architecture and put the model on a robot, which is able to mimic the child's hiding behavior. They also take the "hiding" model and use it as the basis for a "seeking" model. They suggest that using the same representations and procedures that a person uses allows better interaction between the human and robotic system.

For the last few years, our lab has been attempting to build robots that are similar to humans in a variety of ways. Our goal has been to build systems that think and act like a person rather than look like a person since the state of the art is not sufficient for a robot to look (even superficially) like a human person. We believe that there are at least two reasons to build robots that think and act like a human. First, how an artificial system acts has a profound effect on how people act toward the system. Second, how an artificial system thinks has a profound effect on how people interact with the system., Published in Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation as Chapter 10, p252-278, 2006. ISBN 0521839645 (Hardcover).

Auditory perspective taking is imagining being in another's place, and predicting what they are able to hear and how it will affect their general comprehension. From this knowledge of another's auditory perspective, a conversational partner can then adapt his or her auditory output to overcome a variety of environmental challenges and insure that what is said is intelligible. In collaborative activities, perspective taking skills greatly facilitate the effort participants must make in communicating with each other. By focusing on perspective taking in the auditory domain, one seeks to allow a robot to use its knowledge of the environment, both a priori and sensed, to predict what its human counterpart can hear and effectively understand. Equipped with this knowledge, the robot can change its auditory presentation behavior accordingly. In the case of the helicopter flying overhead, for instance, the robot should be able to predict the inability of its addressee to adequately hear what it is saying and either try to talk louder or simply pause until noise levels return to normal. Either option is something that is easily implemented, and which could go far towards improving the quality of human-robot interaction. In general, a robot capable of auditory perspective taking could pay attention to a number of different factors that might affect human robot communication: masking noises, interruptions/distractions, changes in operator or robot position, and individual differences between different operators. As people can adapt to each of these naturally, a robot speech interface unable to similarly adapt is likely to prove frustrating to a human partner. In this poster presentation, the authors explore the concept of auditory perspective taking using an audio-visual robot speech interface., Prepared in cooperation with the Georgia Institute of Technology, Atlanta, GA. The original document contains color images. Work Order No. N0001405WX30022.

We propose that an important aspect of human-robot interaction is perspective-taking. We show how perspective-taking occurs in a naturalistic environment (astronauts working on a collaborative project) and present a cognitive architecture for performing perspective-taking called Polyscheme. Finally, we show a fully integrated system that instantiates our theoretical framework within a working robot system. Our system successfully solves a series of perspective-taking problems and uses the same frames of references that astronauts do to facilitate collaborative problem solving with a person., Published in the IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans v35, n4, Jul 2005. Sponsored in part by DAPRA.

The authors examine the effects of environmental cues on being interrupted while performing a task. They conducted an experiment in which participants, after an interruption, received either a blatant environmental cue of their previous action (a red arrow), a subtle environmental cue of their previous action (a cursor that was placed in the same location as their previous action), or no environmental cue at all. The authors found that participants in the blatant condition resumed their task faster than participants in the other two conditions. Furthermore, a subtle environmental cue was no better than no cue at all. The results support their model of memory for goals., Presented at the Human Factors and Ergonomics Society Annual Meeting (49th) held in Orlando, FL on 26-30 Sep 2005. Project order number 55-8122-05. Prepared in cooperation with Michigan State University, East Lansing, MI. The original document contains color images.

M%nmnoid robots sllch as Robona%t offer many opporftinities for advancing the llse of robots in complex enviromnents sllch as space, and for developn%nt of more effective interfaces for h%nmns to interact with then%. Once a s%fficiently high level of interaction between robots and h%nmns is achieved, the operation of and interaction with these robots will becon%e less of an additional b%rden for the h%nmns, and n%ore of a collaboration to achieve the ob\ectives of the task-at-hand. In this paper we describe om plans to endow Robona%t with cognitive capabilities which will sllppoIt collaboration between h%nmn astronallts and Robona%t. We bllild llpon ollr experience in naniral l%%%%1%%% %nderstanding, ge sflire recognition, spatial reasoning and cognitive n%odeling in achieving fl%ese goals., Pub. in International Journal of Humanoid Robotics, v2 n2 p181-201, 2005. The original document contains color images.

The effective use of humanoid robots in space will depend upon the efficacy of interaction between humans and robots. The key to achieving this interaction is to provide the robot with sufficient skills for natural communication with humans so that humans can interact with the robot almost as though it were another human. This paper describes the design, implementation, and capabilities of a robotic system architecture for a robot which can be used (at some level) to collaborate with a human. The capabilities required of the robot include voice recognition, natural language understanding, gesture recognition, spatial reasoning, and cognitive modeling with perspective-taking. These represent a small subset of potential capabilities humans utilize with one another in collaborating to perform a task in a complex environment, and barely scratches the surface of capabilities that one might want to build into an intelligent, collaborative robot. The capabilities described here have been successfully implemented and demonstrated on several mobile robotic platforms (Sofge, et al., 2004), and the authors are now porting them to Robonaut. They also are extending the capabilities of the cognitive architectures (both ACT-R/S and Polyscheme) and their perspective-taking cognitive models. Future work will focus on enhancing the cognitive models through expanded rule sets and cognitively plausible behaviors and reasoning mechanisms, and adding learning capabilities to the models so that the robots may be able to acquire new knowledge and skills through interaction with humans and while performing tasks., Published in Proceedings of the 16th IFAC Symposium on Automatic Control in Aerospace, 2004. Prepared in collaboration with the University of Missouri-Columbia, Electrical and Computer Engineering Department, Columbia, MO. The original document contains color images.

In conversation, people often use spatial relationships to describe their environment, e.g., "There is a desk in front of me and a doorway behind it", and to issue directives, e.g., "Go around the desk and through the doorway." In our research, we have been investigating the use of spatial relationships to establish a natural communication mechanism between people and robots, in particular, for novice users. In this paper, the work on robot spatial relationships is combined with a multi-modal robot interface. We show how linguistic spatial descriptions and other spatial information can be extracted from an evidence grid map and how this information can be used in a natural, human-robot dialog. Examples using spatial language are included for both robot-to-human feedback and also human-to-robot commands. We also discuss some linguistic consequences in the semantic representations of spatial and locative information based on this work., The original document contains color images. Prepared in collaboration with University of Missouri-Columbia, Columbia, MD 65211. Pub. in IEEE Transactions on SMC, Part C, Special Issue on Human-Robot Interaction, July 2002. Sponsored in part by DARPA and NRL.

In their research on intuitive means for humans and intelligent, mobile robots to collaborate, the authors use a multimodal interface that supports speech and gestural inputs. As a preliminary step to evaluate their approach and to identify practical areas for future work, they conducted a Wizard-of-Oz pilot study with five participants who each collaborated with a robot on a search task in a separate room. The goal was to find a sign in the robot's environment with the word "FOO" printed on it. Using a subset of their multimodal interface, participants were told to direct the collaboration. As their subordinate, the robot would understand their utterances and gestures, and recognize objects and structures in the search space. Participants conversed with the robot through a wireless microphone and headphone and, for gestural input, used a touch screen displaying alternative views of the robot's environment to indicate locations and objects., Prepared in collaboration with the University of Missouri-Columbia, Computer Engineering and Computer Science Department, Columbia, MO. Sponsored in part by DARPA. The original document contains color images.