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Used together with:
learn (14)
reinforc (10)
averag (7)
base (7)
model (5)

Stem reward$ (all stems)

57 papers:

FMFM-2015-QuatmannJDWAKB
Counterexamples for Expected Rewards (TQ, NJ, CD, RW, , JPK, BB), pp. 435–452.
HCIHCI-IT-2015-GohPL #game studies
An Investigation of Reward Systems in Human Computation Games (DHLG, EPPPT, CSL), pp. 596–607.
ECIRECIR-2015-LuoZDY #design #using
Designing States, Actions, and Rewards for Using POMDP in Session Search (JL, SZ, XD, HY), pp. 526–537.
QAPLQAPL-2015-BortolussiH #markov #modelling #performance
Efficient Checking of Individual Rewards Properties in Markov Population Models (LB, JH), pp. 32–47.
HPCAHPCA-2015-IslamMRW #low cost
Paying to save: Reducing cost of colocation data center via rewards (MAI, AHM, SR, XW), pp. 235–245.
VMCAIVMCAI-2015-BraitlingFHWBH #automaton #markov #metric
Abstraction-Based Computation of Reward Measures for Markov Automata (BB, LMFF, HH, RW, BB, HH), pp. 172–189.
ITiCSEITiCSE-2014-HortonC
Impact of reward structures in an inverted course (DH, JC), p. 341.
HCIDUXU-DI-2014-ChoiCSLY #case study #crowdsourcing #design
A Study about Designing Reward for Gamified Crowdsourcing System (JC, HC, WS, JL, JY), pp. 678–687.
SACSAC-2014-ShoshitaishviliIDV #analysis #scalability #security #trade-off
Do you feel lucky?: a large-scale analysis of risk-rewards trade-offs in cyber security (YS, LI, AD, GV), pp. 1649–1656.
FoSSaCSFoSSaCS-2013-UmmelsB #markov #modelling
Computing Quantiles in Markov Reward Models (MU, CB), pp. 353–368.
ICMLICML-c1-2013-GrinbergP #optimisation
Average Reward Optimization Objective In Partially Observable Domains (YG, DP), pp. 320–328.
ICMLICML-c3-2013-TamarCM #difference
Temporal Difference Methods for the Variance of the Reward To Go (AT, DDC, SM), pp. 495–503.
ICSEICSE-2013-SnipesANM #developer #performance #towards
Towards recognizing and rewarding efficient developer work patterns (WS, VA, ARN, ERMH), pp. 1277–1280.
CHICHI-2012-WilliamsonM
Rewarding the original: explorations in joint user-sensor motion spaces (JW, RMS), pp. 1717–1726.
CIKMCIKM-2012-ZhouCLJ
Evaluating reward and risk for vertical selection (KZ, RC, ML, JMJ), pp. 2631–2634.
SIGIRSIGIR-2012-ZhaoHW #information retrieval #probability
Rewarding term location information to enhance probabilistic information retrieval (JZ, JXH, SW), pp. 1137–1138.
GPCEGPCE-2012-BauerEFP #adaptation #performance
Faster program adaptation through reward attribution inference (TB, ME, AF, JP), pp. 103–111.
ASEASE-2011-Groce #adaptation #generative #programming
Coverage rewarded: Test input generation via adaptation-based programming (AG), pp. 380–383.
ICPCICPC-2011-EndrikatH #aspect-oriented #development #maintenance #programming
Is Aspect-Oriented Programming a Rewarding Investment into Future Code Changes? A Socio-technical Study on Development and Maintenance Time (SE, SH), pp. 51–60.
SEKESEKE-2011-El-KharboutlyG #analysis #architecture #concurrent #probability #reliability #using
Architecture-based Reliability Analysis of Concurrent Software Applications using Stochastic Reward Nets (REK, SSG), pp. 635–639.
QAPLQAPL-2011-DengGHM #probability #process #testing
Real-Reward Testing for Probabilistic Processes (Extended Abstract) (YD, RJvG, MH, CM), pp. 61–73.
CHICHI-2010-BerkovskyCFBB #game studies #physics #process
Physical activity motivating games: virtual rewards for real activity (SB, MC, JF, DB, NB), pp. 243–252.
ICMLICML-2010-LizotteBM #analysis #learning #multi #performance #random
Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis (DJL, MHB, SAM), pp. 695–702.
ICMLICML-2010-SorgSL #bound
Internal Rewards Mitigate Agent Boundedness (JS, SPS, RLL), pp. 1007–1014.
SACSAC-2009-BoucherZ #owl
Leveraging OWL for GIS interoperability: rewards and pitfalls (SB, EZ), pp. 1267–1272.
VMCAIVMCAI-2009-JurdzinskiLR #automaton #game studies #hybrid
Average-Price-per-Reward Games on Hybrid Automata with Strong Resets (MJ, RL, MR), pp. 167–181.
ICALPICALP-A-2008-EtessamiWY #game studies #probability #recursion
Recursive Stochastic Games with Positive Rewards (KE, DW, MY), pp. 711–723.
HCIOCSC-2007-HoislAM #community #social #wiki
Social Rewarding in Wiki Systems — Motivating the Community (BH, WA, SM), pp. 362–371.
HCIOCSC-2007-Jang #community #game studies #online #self
Managing Fairness: Reward Distribution in a Self-organized Online Game Player Community (CYJ), pp. 375–384.
ICMLICML-2007-Marthi #automation #composition
Automatic shaping and decomposition of reward functions (BM), pp. 601–608.
ICMLICML-2007-PetersS #learning
Reinforcement learning by reward-weighted regression for operational space control (JP, SS), pp. 745–750.
ICMLICML-2006-SimsekB #performance
An intrinsic reward mechanism for efficient exploration (ÖS, AGB), pp. 833–840.
KRKR-2006-BonetG #heuristic #using
Heuristics for Planning with Penalties and Rewards using Compiled Knowledge (BB, HG), pp. 452–462.
DACDAC-2005-CortesEP #energy #realtime
Quasi-static assignment of voltages and optional cycles for maximizing rewards in real-time systems with energy c-onstraints (LAC, PE, ZP), pp. 889–894.
ICMLICML-2005-WangLBS #online #optimisation
Bayesian sparse sampling for on-line reward optimization (TW, DJL, MHB, DS), pp. 956–963.
CSEETCSEET-2004-SuriS #challenge #education #process #re-engineering
Incorporating Software Process in an Undergraduate Software Engineering Curriculum: Challenges and Rewards (DS, MJS), pp. 18–23.
ICPRICPR-v4-2004-FrancoMN #editing
Reward-Punishment Editing (AF, DM, LN), pp. 424–427.
ICSEICSE-2004-SoundarajanH #design pattern #specification
Responsibilities and Rewards: Specifying Design Patterns (NS, JOH), pp. 666–675.
HPDCHPDC-2004-IrwinGC
Balancing Risk and Reward in a Market-Based Task Service (DEI, LEG, JSC), pp. 160–169.
ITiCSEITiCSE-2003-Hazzan03a #concept #student
Computer science students’ conception of the relationship between reward (grade) and cooperation (OH), pp. 178–182.
ICMLICML-2003-LaudD #analysis #learning
The Influence of Reward on the Speed of Reinforcement Learning: An Analysis of Shaping (AL, GD), pp. 440–447.
ICMLICML-2002-GhavamzadehM #learning
Hierarchically Optimal Average Reward Reinforcement Learning (MG, SM), pp. 195–202.
ICMLICML-2002-SeriT #learning #modelling
Model-based Hierarchical Average-reward Reinforcement Learning (SS, PT), pp. 562–569.
ICMLICML-2002-ShapiroL #learning #using
Separating Skills from Preference: Using Learning to Program by Reward (DGS, PL), pp. 570–577.
ICMLICML-2001-SatoK #learning #markov #problem
Average-Reward Reinforcement Learning for Variance Penalized Markov Decision Problems (MS, SK), pp. 473–480.
UMLUML-2001-Damm #comprehension #uml
Understanding UML — Pains and Rewards (WD), p. 240.
ICMLICML-2000-GoldbergM #learning #modelling #multi
Learning Multiple Models for Reward Maximization (DG, MJM), pp. 319–326.
ICSEICSE-2000-Colyer #challenge #research
From research to reward: challenges in technology transfer (AMC), pp. 569–576.
ITiCSEITiCSE-1999-ParkerH #industrial #risk management
Campus-based industrial software projects: risks and rewards (HP, MH), p. 189.
ICMLICML-1999-NgHR #policy #theory and practice
Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping (AYN, DH, SJR), pp. 278–287.
ICALPICALP-1997-Bernardo #algebra
An Algebra-Based Method to Associate Rewards with EMPA Terms (MB), pp. 358–368.
ICMLICML-1996-Mahadevan #learning
Sensitive Discount Optimality: Unifying Discounted and Average Reward Reinforcement Learning (SM), pp. 328–336.
ICMLICML-1996-TadepalliO #approximate #domain model #learning #modelling #scalability
Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function (PT, DO), pp. 471–479.
ICMLICML-1995-KimuraYK #learning #probability
Reinforcement Learning by Stochastic Hill Climbing on Discounted Reward (HK, MY, SK), pp. 295–303.
ICMLICML-1995-MoriartyM #evolution #learning #performance
Efficient Learning from Delayed Rewards through Symbiotic Evolution (DEM, RM), pp. 396–404.
ICMLICML-1994-Mataric #learning
Reward Functions for Accelerated Learning (MJM), pp. 181–189.
ICMLICML-1993-Schwartz #learning
A Reinforcement Learning Method for Maximizing Undiscounted Rewards (AS), pp. 298–305.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
Hosted as a part of SLEBOK on GitHub.