4,616 research outputs found
Multi-Agent Task Allocation for Robot Soccer
This is the published version. Copyright De GruyterThis paper models and analyzes task allocation methodologies for multiagent systems. The evaluation process was implemented as a collection of simulated soccer matches. A soccer-simulation software package was used as the test-bed as it provided the necessary features for implementing and testing the methodologies. The methodologies were tested through competitions with a number of available soccer strategies. Soccer game scores, communication, robustness, fault-tolerance, and replanning capabilities were the parameters used as the evaluation criteria for the mul1i-agent systems
Merging plans with incomplete knowledge about actions and goals through an agent-based reputation system
Managing transition plans is one of the major problems of people with
cognitive disabilities. Therefore, finding an automated way to generate such
plans would be a helpful tool for this community. In this paper we have
specifically proposed and compared different alternative ways to merge plans
formed by sequences of actions of unknown similarities between goals and
actions executed by several operator agents which cooperate between them
applying such actions over some passive elements (node agents) that require
additional executions of another plan after some time of use. Such ignorance of
the similarities between plan actions and goals would justify the use of a
distributed recommendation system that would provide an useful plan to be
applied for a certain goal to a given operator agent, generated from the known
results of previous executions of different plans by other operator agents.
Here we provide the general framework of execution (agent system), and the
different merging algorithms applied to this problem. The proposed agent system
would act as an useful cognitive assistant for people with intelectual
disabilities such as autism
Deconstructing the Veneer of Simplicity: Co-Designing Introductory Generative AI Workshops with Local Entrepreneurs
Generative AI platforms and features are permeating many aspects of work.
Entrepreneurs from lean economies in particular are well positioned to
outsource tasks to generative AI given limited resources. In this paper, we
work to address a growing disparity in use of these technologies by building on
a four-year partnership with a local entrepreneurial hub dedicated to equity in
tech and entrepreneurship. Together, we co-designed an interactive workshops
series aimed to onboard local entrepreneurs to generative AI platforms.
Alongside four community-driven and iterative workshops with entrepreneurs
across five months, we conducted interviews with 15 local entrepreneurs and
community providers. We detail the importance of communal and supportive
exposure to generative AI tools for local entrepreneurs, scaffolding actionable
use (and supporting non-use), demystifying generative AI technologies by
emphasizing entrepreneurial power, while simultaneously deconstructing the
veneer of simplicity to address the many operational skills needed for
successful application
Merging plans with incomplete knowledge about actions and goals through an agent-based reputation system
In This Paper, We Propose And Compare Alternative Ways To Merge Plans Formed Of Sequences Of Actions With Unknown Similarities Between The Goals And Actions. Plans Are Formed Of Actions And Are Executed By Several Operator Agents, Which Cooperate Through Recommendations. The Operator Agents Apply The Plan Actions To Passive Elements (Which We Call Node Agents) That Will Require Additional Future Executions Of Other Plans After Some Time. The Ignorance Of The Similarities Between The Plan Actions And The Goals Justifies The Use Of A Distributed Recommendation System To Produce A Useful Plan For A Given Operator Agent To Apply Towards A Certain Goal. This Plan Is Generated From The Known Results Of Previous Executions Of Various Plans By Other Operator Agents. Here, We Present The General Framework Of Execution (The Agent System) And The Results Of Applying Various Merging Algorithms To This Problem.This work was supported in part by Project MINECO TEC2017-88048-C2-2-
Cloud Robotics and Autonomous Vehicles
Recently, a good amount of research has been focused on the development of the autonomous vehicles. Autonomous vehicles possess great potential in numerous challenging applications, for example, autonomous armoured fighting vehicles, automated highway systems, etc. To enable the usage of autonomous vehicles in such challenging applications, it is important to ensure the safety, efficiency, reliability and robustness of the system. Most of the existing implementations of the autonomous vehicles operate as standalone systems limited to onboard capabilities (computations, memory, data, etc.), which limit their potential and performance in real-world applications. The advent of the Internet and emerging advances in the cloud infrastructure suggests new methodologies where vehicles are not limited to onboard capabilities. Processing is also performed remotely on cloud to support different operations and to increase the proficiency of decision-making. This chapter surveys the research to date in the evolution of autonomous vehicles, cloud and cloud-enabled autonomous vehicles, with the limitations of existing systems, research challenges and possible future directions. The chapter can help new researchers in the field to understand and evaluate different approaches for the design of the autonomous vehicular systems
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