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Improving Human Interaction with Autonomous Systems: Supporting Intentionality through Increased Awareness
Abstract
Understanding intentionality is a necessary feature of joint activities involving interdependent agents. This challenge has increased alongside the deployment of autonomous systems that are to some degree unsupervised. This research aims to reduce the number of intentionality recognition breakdowns between people and autonomous systems by designing systems to support the awareness of information cues used in those decisions. The paper outlines theoretical foundations for this approach using simulation theory and process models of intention. The notion of breakdowns is then applied to mistaken intentions in a diary study to gain insight into the phenomena.
... However, as social beings we learn about the world around us through other people, both actively and passively. People have evolved information gathering, decision making, problem solving and anticipatory judgment processes and mechanisms to understand other people's likely actions and intentions because it is advantageous to their own survival and well-being to anticipate the future actions of others [10]. These social processes and mechanisms can certainly assist the proactive/autonomous system to be more focused to achieve its required goals. ...
In this paper, we describe a Bio-SAmI system, which is a Biomedical learning system that is context- aware and responsive to mobile learners sharing information on a social network. Bio-SAmI is a Web 2.0 enabled system which employees Social Ambient Intelligence techniques. The Bio-SAmI infrastructure is based on the Actor model that treats mobile users or “actors" as the universal primitives of computation. The actor model is implemented using a combination of Java enabled APIs including SALSA, JADE, LEAP and tuPrologME. The implemented prototype enable learners to share biomedical information represented by the DICOM SR standard in relation to the notion of inflammation as well as to respond to variety of learning queries including classifying learning case studies, finding learning case studies, locating a FOAF learner and syndication and aggregation of learning case studies .
In this article we consider the ways in which visual information is used as a conversational
resource in the accomplishment of collaborative physical tasks.We focus
on the role of visual information in maintaining task awareness and in achieving
mutual understanding in conversation. We first describe the theoretical
framework we use to analyze the role of visual information in physical collaboration.
Then, we present two experiments that vary the amount and quality of the
visual information available to participants during a collaborative bicycle repair
task. We examine the effects of this visual information on performance and on
conversational strategies. We conclude with a general discussion of how situational
awareness and conversational grounding are achieved in collaborative repair
and with some design considerations for systems to support remote collaborative
repair.
We propose that the crucial difference between human cognition and that of other species is the ability to participate with others in collaborative activities with shared goals and intentions: shared intentionality. Participation in such activities requires not only especially powerful forms of intention reading and cultural learning, but also a unique motivation to share psychological states with others and unique forms of cognitive representation for doing so. The result of participating in these activities is species-unique forms of cultural cognition and evolution, enabling everything from the creation and use of linguistic symbols to the construction of social norms and individual beliefs to the establishment of social institutions. In support of this proposal we argue and present evidence that great apes (and some children with autism) understand the basics of intentional action, but they still do not participate in activities involving joint intentions and attention (shared intentionality). Human children's skills of shared intentionality develop gradually during the first 14 months of life as two ontogenetic pathways intertwine: (1) the general ape line of understanding others as animate, goal-directed, and intentional agents; and (2) a species-unique motivation to share emotions, experience, and activities with other persons. The developmental outcome is children's ability to construct dialogic cognitive representations, which enable them to participate in earnest in the collectivity that is human cognition.
Despite technical advances in CSCW over the past few years we still have relatively little understanding of the organisation of collaborative activity in real world, technologically supported, work environments. Indeed, it has been suggested that the failure of various technological applications may derive from its relative insensitivity to ordinary work practice and situated conduct. In this paper we discuss the possibility of utilising recent developments within social science, and in particular the naturalistic analysis of organisational conduct and interpersonal communication, as a basis for the design and development of tools and technologies to support collaborative work. Focussing on the Line Control Rooms on London Underground, a complex multimedia environment in transition, we begin to explicate the informal work practices and procedures whereby personnel systematically communicate information and coordinate a disparate collection of tasks and activities. These empirical investigations form the foundation to the design of new tools to support collaborative work in Line Control Rooms; technologies which will be sensitive to the ordinary conduct and practical skills of organisational personnel in the London Underground.
In this paper we propose an account of human/computer awareness for use in the (re)design of complex human/computer interaction, before empirically testing its utility. Specifically, having situated our work in the wider field of human/computer awareness research, we address the well-reported phenomenon of "situation awareness" breakdowns in the aviation domain. We assert the need for an explanatory and predictive model of the phenomenon if the frequency of such breakdowns is to be reduced and propose such a model. We then go on to investigate the utility of our model as a guide for design through the discussion of a recent experiment involving manipulations of an animated warning signal on a simulated cockpit control panel. Our results show initial support both for the model and for our assertion of its utility. We conclude that our composite view of awareness yields practical benefit in the design of human computer awareness support.
Systems that can change their behavior in response to unanticipated events during operation are called "autonomous." The capability of such systems and their domains of application have expanded significantly in recent years, with high-profile successes in both civilian and military applications. These successes have also been accompanied by high-profile failures that compellingly illustrate the real technical difficulties associated with seemingly natural behavior specification for truly autonomous systems. The rewards are great for advancing this technology, however. Autonomous systems technology is truly transformational, with potential benefits in both cost and risk reduction. The technology also holds the potential for enabling entirely new capabilities in environments where direct human control is not physically possible. Note also that autonomy development is a discipline that cuts broadly across traditional engineering domains and system life-cycle phases, a true systems engineering discipline. For all of these reasons, APL has identified autonomous systems technology as an important element of its science and technology vision and a critical area for future development.
“The tailor carries the old woman’s groceries across the street.” (Winter & Uleman, 1984)
If you are like most people, upon reading this sentence you made the inference that the tailor was “helpful.” Social psychologists have long argued that such trait inferences are central to social perception and social interaction. Moreover, considerable research demonstrates that people make such trait inferences spontaneously (quickly and with no conscious intentions) in response to a wide array of social events (for a review see Uleman, Newman, & Moskowitz, 1996; also see Carlston & Skowronski, 1994).
But what do such trait inferences have to do with the problem of other minds? A great deal. We, along with several other authors (e.g., Ames, this volume; Reeder and Trafimow, this volume) argue that inferences about the goals and mental states of people are central to making trait inferences about them (also see Jones & Davis, 1965). Ultimately, we are making two related claims here: (1) Inferences about an actor’s goals typically play a central role in making trait ascriptions about them, and (2) trait ascriptions about an actor typically involve making claims about the goals and mental states of the actor.
However, despite the evidence that trait inferences are frequently and spontaneously made and that goal inferences play an important role in trait ascriptions, there are no process models of which we are aware that provide an account of how such spontaneous inferences are made. In this paper, we will outline such a process account of trait inferences, the Social Dynamics model. This account is based on a general model of person perception that we have outlined in greater detail in other places (Read, 1987; Read & Miller, 1993; Read & Miller, 1998). Central to this model is the idea that inferences about an actor’s goals frequently drive trait inferences.
Given business and industry's increased reliance on teams in organizational settings, it is critical that all those who are interested in improving training and performance better understand teams. During the past decade, cognitive science has substantially influenced the study of team performance. The contributors to this volume describe the many ways in which team cognition is being used as an organizing framework to guide research into factors that affect team performance. (PsycINFO Database Record (c) 2012 APA, all rights reserved)