105,594 research outputs found
Random Feature Models for Learning Interacting Dynamical Systems
Particle dynamics and multi-agent systems provide accurate dynamical models
for studying and forecasting the behavior of complex interacting systems. They
often take the form of a high-dimensional system of differential equations
parameterized by an interaction kernel that models the underlying attractive or
repulsive forces between agents. We consider the problem of constructing a
data-based approximation of the interacting forces directly from noisy
observations of the paths of the agents in time. The learned interaction
kernels are then used to predict the agents behavior over a longer time
interval. The approximation developed in this work uses a randomized feature
algorithm and a sparse randomized feature approach. Sparsity-promoting
regression provides a mechanism for pruning the randomly generated features
which was observed to be beneficial when one has limited data, in particular,
leading to less overfitting than other approaches. In addition, imposing
sparsity reduces the kernel evaluation cost which significantly lowers the
simulation cost for forecasting the multi-agent systems. Our method is applied
to various examples, including first-order systems with homogeneous and
heterogeneous interactions, second order homogeneous systems, and a new sheep
swarming system
Monitoring Teams by Overhearing: A Multi-Agent Plan-Recognition Approach
Recent years are seeing an increasing need for on-line monitoring of teams of
cooperating agents, e.g., for visualization, or performance tracking. However,
in monitoring deployed teams, we often cannot rely on the agents to always
communicate their state to the monitoring system. This paper presents a
non-intrusive approach to monitoring by 'overhearing', where the monitored
team's state is inferred (via plan-recognition) from team-members' routine
communications, exchanged as part of their coordinated task execution, and
observed (overheard) by the monitoring system. Key challenges in this approach
include the demanding run-time requirements of monitoring, the scarceness of
observations (increasing monitoring uncertainty), and the need to scale-up
monitoring to address potentially large teams. To address these, we present a
set of complementary novel techniques, exploiting knowledge of the social
structures and procedures in the monitored team: (i) an efficient probabilistic
plan-recognition algorithm, well-suited for processing communications as
observations; (ii) an approach to exploiting knowledge of the team's social
behavior to predict future observations during execution (reducing monitoring
uncertainty); and (iii) monitoring algorithms that trade expressivity for
scalability, representing only certain useful monitoring hypotheses, but
allowing for any number of agents and their different activities to be
represented in a single coherent entity. We present an empirical evaluation of
these techniques, in combination and apart, in monitoring a deployed team of
agents, running on machines physically distributed across the country, and
engaged in complex, dynamic task execution. We also compare the performance of
these techniques to human expert and novice monitors, and show that the
techniques presented are capable of monitoring at human-expert levels, despite
the difficulty of the task
The Simulation Model Partitioning Problem: an Adaptive Solution Based on Self-Clustering (Extended Version)
This paper is about partitioning in parallel and distributed simulation. That
means decomposing the simulation model into a numberof components and to
properly allocate them on the execution units. An adaptive solution based on
self-clustering, that considers both communication reduction and computational
load-balancing, is proposed. The implementation of the proposed mechanism is
tested using a simulation model that is challenging both in terms of structure
and dynamicity. Various configurations of the simulation model and the
execution environment have been considered. The obtained performance results
are analyzed using a reference cost model. The results demonstrate that the
proposed approach is promising and that it can reduce the simulation execution
time in both parallel and distributed architectures
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