An empirical study of argumentation schemes in deliberative dialogue

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Reasoning about Uncertain Information and Conflict Resolution through Trust Revision

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Task switching and cognitively compatible guidance for control of multiple robots

Decision aiding sometimes fails not because following guidance would not improve performance but because humans have difficulty in following guidance as it is presented to them. This paper presents a new analysis of data from multi-robot control experiments in which guidance in a demonstrably superior robot selection strategy failed to produce improvement in performance. We had earlier suggested that the failure to benefit might be related to loss of volition in switching between robots being controlled. In this paper we present new data indicating that spatial, and hence cognitive proximity, of robots may play a role in making volitional switches more effective. Foraging tasks, such as search and rescue or reconnaissance, in which UVs are either relatively sparse and unlikely to interfere with one another or employ automated path planning, form a broad class of applications in which multiple robots can be controlled sequentially in a round-robin fashion. Such human-robot systems can be described as a queuing system in which the human acts as a server while robots presenting requests for service are the jobs. The possibility of improving system performance through well- known scheduling techniques is an immediate consequence. Two experiments investigating scheduling interventions are described. The first compared a system in which all anomalous robots were alarmed (Alarm), one in which alarms were presented singly in the order in which they arrived (FIFO) and a Control condition without alarms. The second experiment employed failures of varying difficulty supporting an optimal shortest job first (SJF) policy. SJF, FIFO, and Alarm conditions were compared. In both experiments performance in directed attention conditions was poorer than predicted. This paper presents new data comparing the spatial proximity in switches between robots selected by the operator (Alarm conditions) and those dictated by the system (FIFO and SJF conditions)

Towards a cognitively realistic computational model of team problem solving using ACT-R agents and the ELICIT experimentation framework

The aim of cognitive social simulation is to improve our understanding of the complex inter-play between factors that are spread across the cognitive, social and technological domains. This makes cognitive social simulation techniques particularly appealing as a means to undertake experiments into socially-distributed cognition. The current paper reports on the results of an ongoing effort to develop a cognitive social simulation capability that can be used to undertake studies into team cognition using the ACT-R cognitive architecture. The focus of the cognitive modeling effort associated with the development of the simulation capability is a particular team-based problem solving task that forms part of the Experimental Laboratory for Investigating Collaboration, Information-sharing, and Trust (ELICIT) experimentation framework. This task has been used with human subjects to investigate the effect of different command and control organizational structures on collective problem solving performance. The results of the cognitive modeling effort are presented and future work to extend both the simulation capability and the cognitive model are outlined. By comparing the results obtained with the ACT-R simulation capability with those obtained from previous experiments using the ELICIT experimentation framework, it should be possible to evaluate the extent to which ACT-R agents exhibit performance profiles similar to those of their human counterparts. This will support the effort to evaluate the extent to which cognitive social simulation experiments with ACT-R can be used to generate findings of predictive and explanatory relevance to future studies using the ELICIT experimentation framework

Problem Restructuring in Negotiation

To achieve movement towards a negotiated settlement, it is often necessary to restructure the problem under negotiation. Problem restructuring can lead to changed perception of the issues by the parties, thus breaking deadlocks and increasing the parties' willingness to compromise. We present a framework and mechanisms for problem restructuring based on the goals and goal relationships of the negotiating parties as well as means of manipulating the parties' utility estimates. In addition, previous negotiations are a source of heuristic advice in the restructuring task. The restructuring approach has been implemented in the PERSUADER, a computer program that acts as a labor mediator in labor management disputes. To achieve its task, the PERSUADER negotiates separately with each party, company and union, to guide them in reaching agreement.negotiation, problem restructuring, persuasive argumentation, case-based reasoning, agent modeling, belief representation