Agent systems that negotiate and learn

Bocionek, Siegfried

doi:10.1006/ijhc.1995.1013

Cited by 18 publications

(8 citation statements)

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“…The RPD system presented does not only provide automated inner induction, but supports these steps by user interaction. This is similar to approaches in the graphics editor domain (Maulsby & Witten, 1993;Cypher, 1991;Sassin, 1994) or learning agents (Bocionek & Sassin, 1993;Bocionek, 1995). It results in safer robot programs that more probably meet the user's intention.…”

Section: Rpd With Generalized Repetitionmentioning

confidence: 52%

“…As a way to overcome this problem we propose dialog-based learning (DBL) (Bocionek & Sassin, 1993), a means to engage the demonstrating person in the induction process by question&answer dialogs that may be initiated by the RPD system as well as by the person. The usefulness of DBL has already been shown in other PbD domains in which--based on only few examples--functions for graphics editors (Sassin, 1994) and personal assistance software (Bocionek, 1995) could be learned (induced).…”

Section: Introductionmentioning

confidence: 99%

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Robot programming by Demonstration (RPD): Supporting the induction by human interaction

Friedrich¹,

Münch²,

Dillmann³

et al. 1996

Mach Learn

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Abstract.Programming by Demonstration (PbD) is a programming method that allows software developers to add new functionalities to a system by simply showing them in the form of few examples. In the robotics domain it has the potential to reduce the amount of time required for programming and also to make programming more "'natural". Just imagine the task of assembling a torch by a manipulator. Wouldn't it be nice to just assemble the torch with one's own hands, watched by video and laser cameras and maybe wearing data gloves, i. e. sensors that provide the data to automatically generate the necessary robot program for the assembly task? And wouldn't it be even nicer to demonstrate the task with few different torches, but achieving an assembly function for all possible variants of them? In order to realize such a PbD environment, at least two major problems have to be solved. First, the sensor data have to be transformed to high-level situation-action descriptors, a task that is not yet solved in general. Second, if a generalization is required, induction algorithms must be applied to the recorded and transformed traces, aiming to find the most general user-intended function from only few examples. In this article we will concentrate only on the second problem. The described experimental environment consists of an industrial robot (PUMA 260b), a 6D teach mouse input device, and some sensors. Various data can be recorded during a demonstration for further processing in the PbD system running on a workstation. The objective is to explore the possibilities of integrating learning and clustering algorithms for automated robot programming. In particular it is investigated how human interaction within system-as well as user-initiated dialogs can support the induction component.

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Section: Rpd With Generalized Repetitionmentioning

confidence: 52%

Section: Introductionmentioning

confidence: 99%

Robot programming by Demonstration (RPD): Supporting the induction by human interaction

Friedrich¹,

Münch²,

Dillmann³

et al. 1996

Mach Learn

View full text Add to dashboard Cite

show abstract

“…Interestingly, similar notions of technologies and social analogies of human-like characteristics and interactions can be found in seminal human-computer interaction (HCI) publications which date back to the 1980s and 1990s, for example 'computers as secretaries' [22], 'computers as dialogue partners' [23], and 'computers as social actors' [24,25]. Scholars in the HCI domain have suggested and then demonstrated in controlled laboratory experiments that people apply social habits, roles, and behaviors to computer technology, either consciously or unconsciously.…”

Section: Robots As Work Partnersmentioning

confidence: 88%

Replacement of Highly Educated Surgical Assistants by Robot Technology in Working Life: Paradigm Shift in the Service Sector

Wasén

2010

Int J of Soc Robotics

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This article highlights the thus far unexplained social and professional effects raised by robotization in surgical applications, and further develops an understanding of social acceptance among professional users of robots in the healthcare sector. It presents findings from ethnographic workplace research on human-robot interactions (HRI) in a population of twenty-three professionals. When considering all the findings, the latest da Vinci system equipped with four robotic arms substitutes two table-side surgical assistants, in contrast to the single-arm AESOP robot that only substitutes one surgical assistant. The adoption of robots and the replacement of surgical assistants provide clear evidence that robots are well-accepted among operating surgeons. Because HRI decrease the operating surgeon's dependence on social assistance and since they replace the work tasks of surgical assistants, the robot is considered a surrogate artificial work partner and worker. This finding is consistent with prior HRI research indicating that users, through their cooperation with robots, often become less reliant on supportive social actions. This research relates to societal issues and provides the first indication that highly educated knowledge workers are beginning to be replaced by robot technology in working life and therefore points towards a paradigm shift in the service sector.

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“…Instructing the agent for even simple tasks can be onerous, and agents who try to infer operators' intent and act autonomously can surprise operators, who might lack mental models of agent behavior. One approach is for the agents to learn and adapt to the characteristics of the operator through a process of remembering what they have been being told to do in similar situations (Bocionek, 1995). After the agent completes a task, it can be equally challenging to make the results meaningful to the operator (Lewis, 1998).…”

Section: Tool Prostheses and Agentsmentioning

confidence: 99%

Human Factors and Ergonomics in Automation Design

Lee

2006

Handbook of Human Factors and Ergonomics

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Agent systems that negotiate and learn

Cited by 18 publications

References 0 publications

Robot programming by Demonstration (RPD): Supporting the induction by human interaction

Robot programming by Demonstration (RPD): Supporting the induction by human interaction

Replacement of Highly Educated Surgical Assistants by Robot Technology in Working Life: Paradigm Shift in the Service Sector

Human Factors and Ergonomics in Automation Design

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