Adjustable Autonomy for the Real World

Scerri, Paul; Pynadath, David V.; Tambe, M.

doi:10.1007/978-1-4419-9198-0_10

Cited by 39 publications

(61 citation statements)

References 15 publications

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“…Scerri's theoretical model of adjustable autonomy is based on "transfer-of-control strategies" [10]. Goodrich's perspective on adjustable autonomy allows for the possibility of automation initiating and terminating itself, but his experimental system, like Scerri's, is based on a model of transitions between human and robot control [11].…”

Section: Related Workmentioning

confidence: 99%

Autonomy mode suggestions for improving human-robot interaction

Baker

Yanco

2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583)

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Section: Related Workmentioning

confidence: 99%

Autonomy mode suggestions for improving human-robot interaction

Baker

Yanco

2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583)

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“…Forward-looking approaches have been used to support questions about both action selection strategy (e.g., Scerri et al, 2002) and learning user preference models (e.g., Boutilier, 2002).…”

Section: Asynchronousmentioning

confidence: 99%

Goal-Directed Metacontrol for Integrated Procedure Learning

Kim¹,

Myers²,

Gervasio³

et al. 2011

Metareasoning

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Developing systems that learn how to perform complex tasks presents a significant challenge to the artificial intelligence community. As the knowledge to be learned becomes complex, with diverse procedural constructs and uncertainties to be validated, the system needs to integrate a wide range of learning and reasoning methods with different focuses and strengths. For example, one learning method may be used to generalize from user demonstrations, another to learn by practice and exploration, and another to test hypotheses with experiments. The POIROT system pursues such a multistrategy learning methodology that employs multiple integrated learners and knowledge validation modules to acquire complex process knowledge for a medical logistics domain (Burstein et al., 2008).For a learning system of such complexity, activities of participating agents must be coordinated to ensure that their collective activities produce the desired procedural knowledge. This kind of control is inherently metalevel (Anderson & Oates, 2007; Cox & Raja, Chapter 1) in that it requires the system to reflect on what it is doing and why, to monitor its progress, and to make adjustments to its 135 behaviour when performance falls short of expectations. Without such introspection, effective coordination and prioritization of the base-level learning and reasoning components would not be possible. This type of introspection corresponds to a form of metareasoning centered on "stepping back" from the system to analyze its behavior, as discussed by Perlis (Chapter 2). As such, it contrasts with the majority of work to date on metareasoning, which has focused on the problem of bounded rationality, as described by Zilberstein (Chapter 3).Developing a metalevel reasoner for such a complex, integrated learning system poses several challenges, including• Assessing the progress of learning over time;• Systematically addressing conflicts and failures that arise during learning;• Addressing gaps and shortcomings of the individual and aggregate learning results;• Supporting flexible interactions among agents that pursue different learning strategies.We describe a metalevel framework for coordinating the activities of a community of learners to create an integrated learning system. The metalevel framework is organized around learning goals, which are formulated through introspective reasoning to identify problems and requirements for the ongoing 136 learning process. These learning goals are posted to a shared blackboard to direct the other components in the system. Goals can be either process or knowledge oriented.Process goals define specific tasks to be performed as part of the learning process and are used to coordinate the activities of the various learning and reasoning components. Examples of process goals for task learning include hypothesis creation, hypothesis merging, explanation of observations, and hypothesis validation through experimentation.Knowledge goals provide the means for a component to convey the need for additional information to f...

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“…A pre-planned sequence of actions either transferring control of a meta-reasoning decision to some entity or taking an action to buy time is called a transfer-of-control strategy. Transferof-control strategies were first introduced and mathematically modeled in [13]. An optimal transfer-of-control strategy optimally balances the risks of not getting a high quality decision against the risk of costs incurred due to a delay in getting that decision.…”

Section: Transfer-of-control Strategiesmentioning

confidence: 99%

“…In this section, we briefly review mathematical model of transfer-of-control strategies presented in [13]. A decision, d, needs to be made.…”

Section: Transfer Of Control Strategiesmentioning

confidence: 99%

“…The second part of our approach is that when a decision is to be made by a human, a transfer-of-control strategy is used to ensure that lack of a timely response does not negatively impact the performance of the team [13]. A transferof-control strategy is a pre-planned sequence of actions that are designed to balance the benefits of getting human input against the costs of that input not coming in a timely manner.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Safety in the Context of Coordination via Adjustable Autonomy

Scerri

Sycara

Tambe

2009

Safety and Security in Multiagent Systems

Self Cite

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Abstract. When large heterogeneous robot and agent teams operate in the real-world, it is essential that a human operator has overall control to ensure safety. However, giving an operator the required control is difficult due to the complexity of the activities such teams engage in and the infeasibility of simply stopping the team whenever human input is required. Our approach to interaction in such a context has three key components which allow us to leverage human expertise by giving them responsibility for key coordination decisions, without risks to the coordination due to slow responses. First, to deal with the dynamic nature of the situation, we use pre-planned sequences of transfer of control actions called transfer-of-control strategies. Second, to allow identification of key coordination issues in a distributed way, individual coordination tasks are explicitly represented as coordination roles, rather than being implicitly represented within a monolithic protocol. Such a representation allows meta-reasoning about those roles to determine when human input may be useful. Third, the meta-reasoning and transfer-of-control strategies are encapsulated in a mobile agent that moves around the group to either get human input or autonomously make a decision. In this paper, we describe this approach and present initial results from interaction between a large number of UAVs and a small number of humans.

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Adjustable Autonomy for the Real World

Cited by 39 publications

References 15 publications

Autonomy mode suggestions for improving human-robot interaction

Autonomy mode suggestions for improving human-robot interaction

Goal-Directed Metacontrol for Integrated Procedure Learning

Safety in the Context of Coordination via Adjustable Autonomy

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