An empirical study of argumentation schemes in deliberative dialogue

Publisher PD

A Process Model of Experience-Based Design

Human designers use previous designs extensively in the process of producing a new design. When they come up with a partial or complete design, designers perform a mental simulation to verify the design.W e present a model for engineering design that integrates case-based reasoning and qualitative simulation. The model involves: (1) setting up the functional requirements, (2) accessing memory to retrieve cases relevant to the requirements, (3) synthesizing pieces of cases into designs, (4) verifying and testing the design, and finally, (5) debugging. This process is apphed recursively till the design is complete and bug free. The model integrates different levels of representation and reasoning mechanisms in order to effectively support the design tasks

Distributed Meeting Scheduling

Meeting scheduling takes place when a group of people intend to meet with each other. Since each person has individual availability constraints and preferences, meeting scheduling is naturally distributed and there is a need to schedule the meeting in such a way as to consider the preferences of the set of meeting participants. In addition, individual meeting constraints and preferences may change both as a result of an agent's situation or as a result of other agents' scheduling decisions. Therefore, there is a need for distributed reactive schedule revision in response to changing requirements and constraints. W e present an approach to distributed meeting scheduling based on modeling and communication of constraints and preferences among the agents. When a feasible global schedule cannot be found, agents enter a negotiation and relax their constraints. The approach enables the agents to find and reach agreement on the schedule with the highest joint utility and to reactively revise the schedule in response to new information

Reasoning about Uncertain Information and Conflict Resolution through Trust Revision

Publisher PD

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)

Research Directions for Service-Oriented Multiagent Systems

Today\u27s service-oriented systems realize many ideas from the research conducted a decade or so ago in multiagent systems. Because these two fields are so deeply connected, further advances in multiagent systems could feed into tomorrow\u27s successful service-oriented computing approaches. This article describes a 15-year roadmap for service-oriented multiagent system research

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