228,828 research outputs found
Dif-MAML: Decentralized Multi-Agent Meta-Learning
The objective of meta-learning is to exploit the knowledge obtained from
observed tasks to improve adaptation to unseen tasks. As such, meta-learners
are able to generalize better when they are trained with a larger number of
observed tasks and with a larger amount of data per task. Given the amount of
resources that are needed, it is generally difficult to expect the tasks, their
respective data, and the necessary computational capacity to be available at a
single central location. It is more natural to encounter situations where these
resources are spread across several agents connected by some graph topology.
The formalism of meta-learning is actually well-suited to this decentralized
setting, where the learner would be able to benefit from information and
computational power spread across the agents. Motivated by this observation, in
this work, we propose a cooperative fully-decentralized multi-agent
meta-learning algorithm, referred to as Diffusion-based MAML or Dif-MAML.
Decentralized optimization algorithms are superior to centralized
implementations in terms of scalability, avoidance of communication
bottlenecks, and privacy guarantees. The work provides a detailed theoretical
analysis to show that the proposed strategy allows a collection of agents to
attain agreement at a linear rate and to converge to a stationary point of the
aggregate MAML objective even in non-convex environments. Simulation results
illustrate the theoretical findings and the superior performance relative to
the traditional non-cooperative setting
Cost Adaptation for Robust Decentralized Swarm Behaviour
Decentralized receding horizon control (D-RHC) provides a mechanism for
coordination in multi-agent settings without a centralized command center.
However, combining a set of different goals, costs, and constraints to form an
efficient optimization objective for D-RHC can be difficult. To allay this
problem, we use a meta-learning process -- cost adaptation -- which generates
the optimization objective for D-RHC to solve based on a set of human-generated
priors (cost and constraint functions) and an auxiliary heuristic. We use this
adaptive D-RHC method for control of mesh-networked swarm agents. This
formulation allows a wide range of tasks to be encoded and can account for
network delays, heterogeneous capabilities, and increasingly large swarms
through the adaptation mechanism. We leverage the Unity3D game engine to build
a simulator capable of introducing artificial networking failures and delays in
the swarm. Using the simulator we validate our method on an example coordinated
exploration task. We demonstrate that cost adaptation allows for more efficient
and safer task completion under varying environment conditions and increasingly
large swarm sizes. We release our simulator and code to the community for
future work.Comment: Accepted to IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS), 201
Game-based learning or game-based teaching?
Emerging technologies for learning report - Article exploring games based learning and its potential for edcuatio
A conceptual architecture for interactive educational multimedia
Learning is more than knowledge acquisition; it often involves the active participation of the learner in a variety of knowledge- and skills-based learning and training activities. Interactive multimedia technology can support the variety of interaction channels and languages required to facilitate interactive learning and teaching.
A conceptual architecture for interactive educational multimedia can support the development of such multimedia systems. Such an architecture needs to embed multimedia technology into a coherent educational context. A framework based on an integrated interaction model is needed to capture learning and training activities in an online setting from an educational perspective, to describe them in the human-computer context, and to integrate them with mechanisms and principles of multimedia interaction
- …