3 research outputs found
Improving the performance of Learned Controllers in Behavior Trees using Value Function Estimates at Switching Boundaries
Behavior trees represent a modular way to create an overall controller from a
set of sub-controllers solving different sub-problems. These sub-controllers
can be created in different ways, such as classical model based control or
reinforcement learning (RL). If each sub-controller satisfies the preconditions
of the next sub-controller, the overall controller will achieve the overall
goal. However, even if all sub-controllers are locally optimal in achieving the
preconditions of the next, with respect to some performance metric such as
completion time, the overall controller might be far from optimal with respect
to the same performance metric. In this paper we show how the performance of
the overall controller can be improved if we use approximations of value
functions to inform the design of a sub-controller of the needs of the next
one. We also show how, under certain assumptions, this leads to a globally
optimal controller when the process is executed on all sub-controllers.
Finally, this result also holds when some of the sub-controllers are already
given, i.e., if we are constrained to use some existing sub-controllers the
overall controller will be globally optimal given this constraint
Multi-Agent Systems
This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019