5,581 research outputs found
Improving the Language Active Learning with Multiagent Systems
Nowadays, there is a growing need for providing novel solutions to facilitate active learning in dependency environments. This paper present a multiagent architecture that incorporates agents specifically designed to provide advanced interfaces for elderly and dependent people that can be executed on mobile devices. The architecture has been initially oriented to language learning in courses for elderly people and has been tested in a real environment. The structure of the architecture and the preliminary results obtained are presented within this paper.Nowadays, there is a growing need for providing novel solutions to facilitate active learning in dependency environments. This paper present a multiagent architecture that incorporates agents specifically designed to provide advanced interfaces for elderly and dependent people that can be executed on mobile devices. The architecture has been initially oriented to language learning in courses for elderly people and has been tested in a real environment. The structure of the architecture and the preliminary results obtained are presented within this paper
Improving the Language Active Learning with Multiagent Systems
Nowadays, there is a growing need for providing novel solutions to facilitate active learning in dependency environments. This paper present a multiagent architecture that incorporates agents specifically designed to provide advanced interfaces for elderly and dependent people that can be executed on mobile devices. The architecture has been initially oriented to language learning in courses for elderly people and has been tested in a real environment. The structure of the architecture and the preliminary results obtained are presented within this paper
Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence
Learning agents that are not only capable of taking tests, but also
innovating is becoming a hot topic in AI. One of the most promising paths
towards this vision is multi-agent learning, where agents act as the
environment for each other, and improving each agent means proposing new
problems for others. However, existing evaluation platforms are either not
compatible with multi-agent settings, or limited to a specific game. That is,
there is not yet a general evaluation platform for research on multi-agent
intelligence. To this end, we introduce Arena, a general evaluation platform
for multi-agent intelligence with 35 games of diverse logics and
representations. Furthermore, multi-agent intelligence is still at the stage
where many problems remain unexplored. Therefore, we provide a building toolkit
for researchers to easily invent and build novel multi-agent problems from the
provided game set based on a GUI-configurable social tree and five basic
multi-agent reward schemes. Finally, we provide Python implementations of five
state-of-the-art deep multi-agent reinforcement learning baselines. Along with
the baseline implementations, we release a set of 100 best agents/teams that we
can train with different training schemes for each game, as the base for
evaluating agents with population performance. As such, the research community
can perform comparisons under a stable and uniform standard. All the
implementations and accompanied tutorials have been open-sourced for the
community at https://sites.google.com/view/arena-unity/
Planning and scheduling research at NASA Ames Research Center
Planning and scheduling is the area of artificial intelligence research that focuses on the determination of a series of operations to achieve some set of (possibly) interacting goals and the placement of those operations in a timeline that allows them to be accomplished given available resources. Work in this area at the NASA Ames Research Center ranging from basic research in constrain-based reasoning and machine learning, to the development of efficient scheduling tools, to the application of such tools to complex agency problems is described
Adaptive multiagent system for seismic emergency management
Presently, most multiagent frameworks are typically programmed in Java. Since the JADE platform has been recently ported to .NET, we used it to create an adaptive multiagent system where the knowledge base of the agents is managed using the CLIPS language, also called from .NET. The multiagent system is applied to create seismic risk scenarios, simulations of emergency situations, in which different parties, modeled as adaptive agents, interact and cooperate.adaptive systems, risk management, seisms.
A survey of agent-oriented methodologies
This article introduces the current agent-oriented methodologies. It discusses what approaches have been followed (mainly extending existing object oriented and knowledge engineering methodologies), the suitability of these approaches for agent modelling, and some conclusions drawn from the survey
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