Autonomy and interdependence in human-agent-robot teams

Johnson, Matthew; Bradshaw, Jeffrey M.; Feltovich, Paul J.; Jonker, Catholijn M.; Riemsdijk, M. Birna van; Sierhuis, Maarten

doi:10.1109/mis.2012.1

Cited by 53 publications

(36 citation statements)

References 11 publications

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“…11 However, one of the most striking recommendations of the DSB report on the role of autonomy is its 6 When striving to maintain an effective balance between self-sufficiency and self-directedness for highly capable machines, designers encounter the additional challenge of making the machine understandable.…”

Section: Myth 2: the Conceptualization Of "Levels Of Autonomy" Is A Umentioning

confidence: 99%

The Seven Deadly Myths of "Autonomous Systems"

et al. 2013

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Section: Myth 2: the Conceptualization Of "Levels Of Autonomy" Is A Umentioning

confidence: 99%

The Seven Deadly Myths of "Autonomous Systems"

et al. 2013

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“…In future work, our particular interest is in the use of common ground to improve collaboration between human-agent teams, and in developing intelligent agents that are capable of interdependent problem solving with humans (Kiesler, 2005;Johnson et al, 2012b;Pfau, Miller, & Sonenberg, 2014;Singh, Miller, & Sonenberg, 2014). As well as modelling explicit communication leading to the establishment of common ground, we are interested in how both the humans and agents can infer common ground implicitly to gain shared awareness of situations.…”

Section: Discussionmentioning

confidence: 99%

“…While Stubbs et al (2007) and several other authors have argued the contribution of common ground to human-agent collaboration (Lee et al, 2011), and its importance to shared situation awareness (Johnson et al, 2012a), there is no precise definition of common ground. Moreover, informal conceptions of the notion vary substantially (Lee, 2001;Allan, 2013).…”

Section: Introductionmentioning

confidence: 99%

Logics of Common Ground

Miller¹,

Pfau²,

Sonenberg³

et al. 2017

jair

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According to Clark's seminal work on common ground and grounding, participants collaborating in a joint activity rely on their shared information, known as common ground, to perform that activity successfully, and continually align and augment this information during their collaboration. Similarly, teams of human and artificial agents require common ground to successfully participate in joint activities. Indeed, without appropriate information being shared, using agent autonomy to reduce the workload on humans may actually increase workload as the humans seek to understand why the agents are behaving as they are. While many researchers have identified the importance of common ground in artificial intelligence, there is no precise definition of common ground on which to build the foundational aspects of multi-agent collaboration. In this paper, building on previously-defined modal logics of belief, we present logic definitions for four different types of common ground. We define modal logics for three existing notions of common ground and introduce a new notion of common ground, called salient common ground. Salient common ground captures the common ground of a group participating in an activity and is based on the common ground that arises from that activity as well as on the common ground they shared prior to the activity. We show that the four definitions share some properties, and our analysis suggests possible refinements of the existing informal and semi-formal definitions.

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“…Lessons learned from collaborative human-robot teams indicate that it is important to be able to escalate to the meta-level (i.e. have humans participate) when necessary [43]. The need for escalating to a higher authority applies whenever a negotiator represents a group or a company (e.g., a union, or stakeholder organizations in general).…”

Section: User Trust and Adoptionmentioning

confidence: 99%

“…[29,43]) might become essential to achieve the best outcome in complex, real-life negotiations. In this envisioned line of research, each negotiation party consists of at least one human and one negotiation agent.…”

Section: User Trust and Adoptionmentioning

confidence: 99%

Computers That Negotiate on Our Behalf: Major Challenges for Self-sufficient, Self-directed, and Interdependent Negotiating Agents

Baarslag

Kaisers

Gerding

et al. 2017

Autonomous Agents and Multiagent Systems

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Abstract.Computers that negotiate on our behalf hold great promise for the future and will even become indispensable in emerging application domains such as the smart grid, autonomous driving, and the Internet of Things. Much research has thus been expended to create agents that are able to negotiate in an abundance of circumstances. However, up until now, truly autonomous negotiators have rarely been deployed in realworld applications. This paper sizes up current negotiating agents and explores a number of technological, societal and ethical challenges that autonomous negotiation systems are bringing about. The questions we address are: in what sense are these systems autonomous, what has been holding back their further proliferation, and is their spread something we should encourage? We relate the automated negotiation research agenda to dimensions of autonomy and distill three major themes that we believe will propel autonomous negotiation forward: accurate representation, long-term perspective, and user trust. We argue these orthogonal research directions need to be aligned and advanced in unison to sustain tangible progress in the field.

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Autonomy and interdependence in human-agent-robot teams

Abstract: H u m a n -a g e n t -R o b o t t e a m w o R k

Cited by 53 publications

References 11 publications

The Seven Deadly Myths of "Autonomous Systems"

The Seven Deadly Myths of "Autonomous Systems"

Logics of Common Ground

Computers That Negotiate on Our Behalf: Major Challenges for Self-sufficient, Self-directed, and Interdependent Negotiating Agents

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