396 research outputs found
Hardness of longest common subsequence for sequences with bounded run-lengths
International audienceThe longest common subsequence (LCS) problem is a classic and well-studied problem in computer science with extensive applications in diverse areas ranging from spelling error corrections to molecular biology. This paper focuses on LCS for fixed alphabet size and fixed run-lengths (i.e., maximum number of consecutive occurrences of the same symbol). We show that LCS is NP-complete even when restricted to (i) alphabets of size 3 and run-length at most 1, and (ii) alphabets of size 2 and run-length at most 2 (both results are tight). For the latter case, we show that the problem is approximable within ratio 3/5
Evolutionary Algorithms for Reinforcement Learning
There are two distinct approaches to solving reinforcement learning problems,
namely, searching in value function space and searching in policy space.
Temporal difference methods and evolutionary algorithms are well-known examples
of these approaches. Kaelbling, Littman and Moore recently provided an
informative survey of temporal difference methods. This article focuses on the
application of evolutionary algorithms to the reinforcement learning problem,
emphasizing alternative policy representations, credit assignment methods, and
problem-specific genetic operators. Strengths and weaknesses of the
evolutionary approach to reinforcement learning are presented, along with a
survey of representative applications
Small Instantons in and Sigma Models
The anomalous scaling behavior of the topological susceptibility in
two-dimensional sigma models for is studied using the
overlap Dirac operator construction of the lattice topological charge density.
The divergence of in these models is traced to the presence of small
instantons with a radius of order (= lattice spacing), which are directly
observed on the lattice. The observation of these small instantons provides
detailed confirmation of L\"{u}scher's argument that such short-distance
excitations, with quantized topological charge, should be the dominant
topological fluctuations in and , leading to a divergent
topological susceptibility in the continuum limit. For the \CP models with
the topological susceptibility is observed to scale properly with the
mass gap. These larger models are not dominated by instantons, but rather
by coherent, one-dimensional regions of topological charge which can be
interpreted as domain wall or Wilson line excitations and are analogous to
D-brane or ``Wilson bag'' excitations in QCD. In Lorentz gauge, the small
instantons and Wilson line excitations can be described, respectively, in terms
of poles and cuts of an analytic gauge potential.Comment: 33 pages, 12 figure
Prototype system for supporting the incremental modelling of vague geometric configurations
In this paper the need for Intelligent Computer Aided Design (Int.CAD) to jointly support design and learning assistance is introduced. The paper focuses on presenting and exploring the possibility of realizing learning assistance in Int.CAD by introducing a new concept called Shared Learning. Shared Learning is proposed to empower CAD tools with more useful learning capabilities than that currently available and thereby provide a stronger interaction of learning between a designer and a computer. Controlled computational learning is proposed as a means whereby the Shared Learning concept can be realized. The viability of this new concept is explored by using a system called PERSPECT. PERSPECT is a preliminary numerical design tool aimed at supporting the effective utilization of numerical experiential knowledge in design. After a detailed discussion of PERSPECT's numerical design support, the paper presents the results of an evaluation that focuses on PERSPECT's implementation of controlled computational learning and ability to support a designer's need to learn. The paper then discusses PERSPECT's potential as a tool for supporting the Shared Learning concept by explaining how a designer and PERSPECT can jointly learn. There is still much work to be done before the full potential of Shared Learning can be realized. However, the authors do believe that the concept of Shared Learning may hold the key to truly empowering learning in Int.CAD
Task-Driven Hybrid Model Reduction for Dexterous Manipulation
In contact-rich tasks, like dexterous manipulation, the hybrid nature of
making and breaking contact creates challenges for model representation and
control. For example, choosing and sequencing contact locations for in-hand
manipulation, where there are thousands of potential hybrid modes, is not
generally tractable. In this paper, we are inspired by the observation that far
fewer modes are actually necessary to accomplish many tasks. Building on our
prior work learning hybrid models, represented as linear complementarity
systems, we find a reduced-order hybrid model requiring only a limited number
of task-relevant modes. This simplified representation, in combination with
model predictive control, enables real-time control yet is sufficient for
achieving high performance. We demonstrate the proposed method first on
synthetic hybrid systems, reducing the mode count by multiple orders of
magnitude while achieving task performance loss of less than 5%. We also apply
the proposed method to a three-fingered robotic hand manipulating a previously
unknown object. With no prior knowledge, we achieve state-of-the-art
closed-loop performance within a few minutes of online learning, by collecting
only a few thousand environment samples.Comment: Reproducing code:
https://github.com/wanxinjin/Task-Driven-Hybrid-Reduction. This is a
preprint. The published version can be accessed at IEEE Transactions on
Robotic
MILCS: A mutual information learning classifier system
This paper introduces a new variety of learning classifier system (LCS), called MILCS, which utilizes mutual information as fitness feedback. Unlike most LCSs, MILCS is specifically designed for supervised learning. MILCS's design draws on an analogy to the structural learning approach of cascade correlation networks. We present preliminary results, and contrast them to results from XCS. We discuss the explanatory power of the resulting rule sets, and introduce a new technique for visualizing explanatory power. Final comments include future directions for this research, including investigations in neural networks and other systems. Copyright 2007 ACM
HPRoP: Hierarchical Privacy-preserving Route Planning For Smart Cities
Route Planning Systems (RPS) are a core component of autonomous personal transport systems essential for safe and efficient navigation of dynamic urban environments with the support of edge-based smart city infrastructure, but they also raise concerns about user route privacy in the context of both privately owned and commercial vehicles. Numerous high-profile data breaches in recent years have fortunately motivated research on privacy preserving RPS, but most of them are rendered impractical by greatly increased communication and processing overhead. We address this by proposing an approach called Hierarchical Privacy-Preserving Route Planning (HPRoP), which divides and distributes the route-planning task across multiple levels and protects locations along the entire route. This is done by combining Inertial Flow partitioning, Private Information Retrieval (PIR), and Edge Computing techniques with our novel route-planning heuristic algorithm. Normalized metrics were also formulated to quantify the privacy of the source/destination points (endpoint location privacy) and the route itself (route privacy). Evaluation on a simulated road network showed that HPRoP reliably produces routes differing only by ≤ 20% in length from optimal shortest paths, with completion times within ∼25 seconds, which is reasonable for a PIR-based approach. On top of this, more than half of the produced routes achieved near-optimal endpoint location privacy (∼1.0) and good route privacy (≥ 0.8)
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