48,448 research outputs found
Improved Memory-Bounded Dynamic Programming for Decentralized POMDPs
Memory-Bounded Dynamic Programming (MBDP) has proved extremely effective in
solving decentralized POMDPs with large horizons. We generalize the algorithm
and improve its scalability by reducing the complexity with respect to the
number of observations from exponential to polynomial. We derive error bounds
on solution quality with respect to this new approximation and analyze the
convergence behavior. To evaluate the effectiveness of the improvements, we
introduce a new, larger benchmark problem. Experimental results show that
despite the high complexity of decentralized POMDPs, scalable solution
techniques such as MBDP perform surprisingly well.Comment: Appears in Proceedings of the Twenty-Third Conference on Uncertainty
in Artificial Intelligence (UAI2007
Neural Network Memory Architectures for Autonomous Robot Navigation
This paper highlights the significance of including memory structures in
neural networks when the latter are used to learn perception-action loops for
autonomous robot navigation. Traditional navigation approaches rely on global
maps of the environment to overcome cul-de-sacs and plan feasible motions. Yet,
maintaining an accurate global map may be challenging in real-world settings. A
possible way to mitigate this limitation is to use learning techniques that
forgo hand-engineered map representations and infer appropriate control
responses directly from sensed information. An important but unexplored aspect
of such approaches is the effect of memory on their performance. This work is a
first thorough study of memory structures for deep-neural-network-based robot
navigation, and offers novel tools to train such networks from supervision and
quantify their ability to generalize to unseen scenarios. We analyze the
separation and generalization abilities of feedforward, long short-term memory,
and differentiable neural computer networks. We introduce a new method to
evaluate the generalization ability by estimating the VC-dimension of networks
with a final linear readout layer. We validate that the VC estimates are good
predictors of actual test performance. The reported method can be applied to
deep learning problems beyond robotics
Learning and Reasoning for Robot Sequential Decision Making under Uncertainty
Robots frequently face complex tasks that require more than one action, where
sequential decision-making (SDM) capabilities become necessary. The key
contribution of this work is a robot SDM framework, called LCORPP, that
supports the simultaneous capabilities of supervised learning for passive state
estimation, automated reasoning with declarative human knowledge, and planning
under uncertainty toward achieving long-term goals. In particular, we use a
hybrid reasoning paradigm to refine the state estimator, and provide
informative priors for the probabilistic planner. In experiments, a mobile
robot is tasked with estimating human intentions using their motion
trajectories, declarative contextual knowledge, and human-robot interaction
(dialog-based and motion-based). Results suggest that, in efficiency and
accuracy, our framework performs better than its no-learning and no-reasoning
counterparts in office environment.Comment: In proceedings of 34th AAAI conference on Artificial Intelligence,
202
CAIR: Using Formal Languages to Study Routing, Leaking, and Interception in BGP
The Internet routing protocol BGP expresses topological reachability and
policy-based decisions simultaneously in path vectors. A complete view on the
Internet backbone routing is given by the collection of all valid routes, which
is infeasible to obtain due to information hiding of BGP, the lack of
omnipresent collection points, and data complexity. Commonly, graph-based data
models are used to represent the Internet topology from a given set of BGP
routing tables but fall short of explaining policy contexts. As a consequence,
routing anomalies such as route leaks and interception attacks cannot be
explained with graphs.
In this paper, we use formal languages to represent the global routing system
in a rigorous model. Our CAIR framework translates BGP announcements into a
finite route language that allows for the incremental construction of minimal
route automata. CAIR preserves route diversity, is highly efficient, and
well-suited to monitor BGP path changes in real-time. We formally derive
implementable search patterns for route leaks and interception attacks. In
contrast to the state-of-the-art, we can detect these incidents. In practical
experiments, we analyze public BGP data over the last seven years
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