652 research outputs found
Learning Combinatorial Interaction Test Generation Strategies Using Hyperheuristic Search
The surge of search based software engineering research has been hampered by the need to develop customized search algorithms for different classes of the same problem. For instance, two decades of bespoke Combinatorial Interaction Testing (CIT) algorithm development, our exemplar problem, has left software engineers with a bewildering choice of CIT techniques, each specialized for a particular task. This paper proposes the use of a single hyperheuristic algorithm that learns search strategies across a broad range of problem instances, providing a single generalist approach. We have developed a Hyperheuristic algorithm for CIT, and report experiments that show that our algorithm competes with known best solutions across constrained and unconstrained problems: For all 26 real-world subjects, it equals or outperforms the best result previously reported in the literature. We also present evidence that our algorithm's strong generic performance results from its unsupervised learning. Hyperheuristic search is thus a promising way to relocate CIT design intelligence from human to machine
Learning with con gurable operators and RL-based heuristics
In this paper, we push forward the idea of machine learning
systems for which the operators can be modi ed and netuned for each
problem. This allows us to propose a learning paradigm where users can
write (or adapt) their operators, according to the problem, data representation
and the way the information should be navigated. To achieve
this goal, data instances, background knowledge, rules, programs and
operators are all written in the same functional language, Erlang. Since
changing operators a ect how the search space needs to be explored,
heuristics are learnt as a result of a decision process based on reinforcement
learning where each action is de ned as a choice of operator and
rule. As a result, the architecture can be seen as a `system for writing
machine learning systems' or to explore new operators.This work was supported by the MEC projects CONSOLIDER-INGENIO 26706 and
TIN 2010-21062-C02-02, GVA project PROMETEO/2008/051, and the REFRAME
project granted by the European Coordinated Research on Long-term Challenges in
Information and Communication Sciences & Technologies ERA-Net (CHIST-ERA),
and funded by the Ministerio de Econom´ıa y Competitividad in Spain. Also, F.
Mart´ınez-Plumed is supported by FPI-ME grant BES-2011-045099MartĂnez Plumed, F.; Ferri RamĂrez, C.; Hernández Orallo, J.; RamĂrez Quintana, MJ. (2013). Learning with con gurable operators and RL-based heuristics. En New Frontiers in Mining Complex Patterns. Springer Verlag (Germany). 7765:1-16. https://doi.org/10.1007/978-3-642-37382-4_1S1167765Armstrong, J.: A history of erlang. In: Proceedings of the Third ACM SIGPLAN Conf. on History of Programming Languages, HOPL III, pp. 1–26. ACM (2007)Brazdil, P., Giraud-Carrier: Metalearning: Concepts and systems. In: Metalearning. Cognitive Technologies, pp. 1–10. Springer, Heidelberg (2009)DaumĂ© III, H., Langford, J.: Search-based structured prediction (2009)Dietterich, T., Domingos, P., Getoor, L., Muggleton, S., Tadepalli, P.: Structured machine learning: the next ten years. Machine Learning 73, 3–23 (2008)Dietterich, T.G., Lathrop, R., Lozano-Perez, T.: Solving the multiple-instance problem with axis-parallel rectangles. Artificial Intelligence 89, 31–71 (1997)DĹľeroski, S.: Towards a general framework for data mining. In: DĹľeroski, S., Struyf, J. (eds.) KDID 2006. LNCS, vol. 4747, pp. 259–300. Springer, Heidelberg (2007)Dzeroski, S., De Raedt, L., Driessens, K.: Relational reinforcement learning. Machine Learning 43, 7–52 (2001), 10.1023/A:1007694015589Dzeroski, S., Lavrac, N. (eds.): Relational Data Mining. Springer (2001)Estruch, V., Ferri, C., Hernández-Orallo, J., RamĂrez-Quintana, M.J.: Similarity functions for structured data. an application to decision trees. Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial 10(29), 109–121 (2006)Estruch, V., Ferri, C., Hernández-Orallo, J., RamĂrez-Quintana, M.J.: Web categorisation using distance-based decision trees. ENTCS 157(2), 35–40 (2006)Estruch, V., Ferri, C., Hernández-Orallo, J., RamĂrez-Quintana, M.J.: Bridging the Gap between Distance and Generalisation. Computational Intelligence (2012)Ferri-RamĂrez, C., Hernández-Orallo, J., RamĂrez-Quintana, M.J.: Incremental learning of functional logic programs. In: Kuchen, H., Ueda, K. (eds.) FLOPS 2001. LNCS, vol. 2024, pp. 233–247. Springer, Heidelberg (2001)Gärtner, T.: Kernels for Structured Data. PhD thesis, Universitat Bonn (2005)Holland, J.H., Booker, L.B., Colombetti, M., Dorigo, M., Goldberg, D.E., Forrest, S., Riolo, R.L., Smith, R.E., Lanzi, P.L., Stolzmann, W., Wilson, S.W.: What is a learning classifier system? In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 1999. LNCS (LNAI), vol. 1813, pp. 3–32. Springer, Heidelberg (2000)Holmes, J.H., Lanzi, P., Stolzmann, W.: Learning classifier systems: New models, successful applications. Information Processing Letters (2002)Kitzelmann, E.: Inductive programming: A survey of program synthesis techniques. In: Schmid, U., Kitzelmann, E., Plasmeijer, R. (eds.) AAIP 2009. LNCS, vol. 5812, pp. 50–73. Springer, Heidelberg (2010)Koller, D., Sahami, M.: Hierarchically classifying documents using very few words. In: Proceedings of the Fourteenth International Conference on Machine Learning, ICML 1997, pp. 170–178. Morgan Kaufmann Publishers Inc., San Francisco (1997)Lafferty, J., McCallum, A.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: ICML 2001, pp. 282–289 (2001)Lloyd, J.W.: Knowledge representation, computation, and learning in higher-order logic (2001)Maes, F., Denoyer, L., Gallinari, P.: Structured prediction with reinforcement learning. Machine Learning Journal 77(2-3), 271–301 (2009)MartĂnez-Plumed, F., Estruch, V., Ferri, C., Hernández-Orallo, J., RamĂrez-Quintana, M.J.: Newton trees. In: Li, J. (ed.) AI 2010. LNCS, vol. 6464, pp. 174–183. Springer, Heidelberg (2010)Muggleton, S.: Inverse entailment and Progol. New Generation Computing (1995)Muggleton, S.H.: Inductive logic programming: Issues, results, and the challenge of learning language in logic. Artificial Intelligence 114(1-2), 283–296 (1999)Plotkin, G.: A note on inductive generalization. Machine Intelligence 5 (1970)Schmidhuber, J.: Optimal ordered problem solver. Maching Learning 54(3), 211–254 (2004)Srinivasan, A.: The Aleph Manual (2004)Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press (1998)Tadepalli, P., Givan, R., Driessens, K.: Relational reinforcement learning: An overview. In: Proc. of the Workshop on Relational Reinforcement Learning (2004)Tamaddoni-Nezhad, A., Muggleton, S.: A genetic algorithms approach to ILP. In: Matwin, S., Sammut, C. (eds.) ILP 2002. LNCS (LNAI), vol. 2583, pp. 285–300. Springer, Heidelberg (2003)Tsochantaridis, I., Hofmann, T., Joachims, T., Altun, Y.: Support vector machine learning for interdependent and structured output spaces. In: ICML (2004)Wallace, C.S., Dowe, D.L.: Refinements of MDL and MML coding. Comput. J. 42(4), 330–337 (1999)Watkins, C., Dayan, P.: Q-learning. Machine Learning 8, 279–292 (1992
Routing Arena: A Benchmark Suite for Neural Routing Solvers
Neural Combinatorial Optimization has been researched actively in the last
eight years. Even though many of the proposed Machine Learning based approaches
are compared on the same datasets, the evaluation protocol exhibits essential
flaws and the selection of baselines often neglects State-of-the-Art Operations
Research approaches. To improve on both of these shortcomings, we propose the
Routing Arena, a benchmark suite for Routing Problems that provides a seamless
integration of consistent evaluation and the provision of baselines and
benchmarks prevalent in the Machine Learning- and Operations Research field.
The proposed evaluation protocol considers the two most important evaluation
cases for different applications: First, the solution quality for an a priori
fixed time budget and secondly the anytime performance of the respective
methods. By setting the solution trajectory in perspective to a Best Known
Solution and a Base Solver's solutions trajectory, we furthermore propose the
Weighted Relative Average Performance (WRAP), a novel evaluation metric that
quantifies the often claimed runtime efficiency of Neural Routing Solvers. A
comprehensive first experimental evaluation demonstrates that the most recent
Operations Research solvers generate state-of-the-art results in terms of
solution quality and runtime efficiency when it comes to the vehicle routing
problem. Nevertheless, some findings highlight the advantages of neural
approaches and motivate a shift in how neural solvers should be conceptualized
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End-to-end deep reinforcement learning in computer systems
Abstract
The growing complexity of data processing systems has long led systems designers to imagine systems (e.g. databases, schedulers) which can self-configure and adapt based on environmental cues. In this context, reinforcement learning (RL) methods have since their inception appealed to systems developers. They promise to acquire complex decision policies from raw feedback signals. Despite their conceptual popularity, RL methods are scarcely found in real-world data processing systems. Recently, RL has seen explosive growth in interest due to high profile successes when utilising large neural networks (deep reinforcement learning). Newly emerging machine learning frameworks and powerful hardware accelerators have given rise to a plethora of new potential applications.
In this dissertation, I first argue that in order to design and execute deep RL algorithms efficiently, novel software abstractions are required which can accommodate the distinct computational patterns of communication-intensive and fast-evolving algorithms. I propose an architecture which decouples logical algorithm construction from local and distributed execution semantics. I further present RLgraph, my proof-of-concept implementation of this architecture. In RLgraph, algorithm developers can explore novel designs by constructing a high-level data flow graph through combination of logical components. This dataflow graph is independent of specific backend frameworks or notions of execution, and is only later mapped to execution semantics via a staged build process. RLgraph enables high-performing algorithm implementations while maintaining flexibility for rapid prototyping.
Second, I investigate reasons for the scarcity of RL applications in systems themselves. I argue that progress in applied RL is hindered by a lack of tools for task model design which bridge the gap between systems and algorithms, and also by missing shared standards for evaluation of model capabilities. I introduce Wield, a first-of-its-kind tool for incremental model design in applied RL. Wield provides a small set of primitives which decouple systems interfaces and deployment-specific configuration from representation. Core to Wield is a novel instructive experiment protocol called progressive randomisation which helps practitioners to incrementally evaluate different dimensions of non-determinism. I demonstrate how Wield and progressive randomisation can be used to reproduce and assess prior work, and to guide implementation of novel RL applications
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