JP5963320B2 - 情報処理装置、情報処理方法、及び、プログラム - Google Patents
情報処理装置、情報処理方法、及び、プログラム Download PDFInfo
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Description
[非特許文献1]A. Labbi and C. Berrospi. Optimizing marketing planning and budgeting using Markov decision processes: An airline case study. IBM Journal of Research and Development, 51(3):421-432, 2007
[非特許文献2]N. Abe, N. K. Verma, C. Apt´e, and R. Schroko. Cross channel optimized marketing by reinforcement learning. In Proceedings of the 10th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2004), pages 767-772, 2004
[非特許文献3]G. Tirenni, A. Labbi, C. Berrospi, A. Elisseeff, T. Bhose, K. Pauro, S. Poyhonen, "The 2005 ISMS Practice Prize Winner - Customer Equity and Lifetime Management (CELM) Finnair Case Study," Marketing Science, vol. 26, no. 4, pp. 553-565, 2007.
[特許文献1]特開2010−191963号公報
[特許文献2]特表2011−513817号公報
[特許文献3]特開2012−190062号公報
Claims (12)
- 各状態にある対象数が施策に応じて遷移する遷移モデルにおける施策を最適化する情報処理装置であって、
複数時点および複数状態の少なくとも一方に亘る施策の合計コストを制約するコスト制約を含む複数のコスト制約を取得するコスト制約取得部と、
各時点および各状態における施策の配分を最適化対象の変数とし、各時点および各状態における施策の適用対象数と前記遷移モデルによる状態遷移に応じた各時点および各状態の推定対象数との間の誤差に応じた項を全期間の総報酬から減じた目的関数を、前記複数のコスト制約を満たしつつ最大化する処理部と、
前記目的関数を最大化する各時点および各状態における施策の配分を出力する出力部と、
を備える情報処理装置。 - 前記処理部は、各時点および各状態における、施策の配分および前記誤差の範囲を最適化対象の変数として前記目的関数を最大化する請求項1に記載の情報処理装置。
- 前記処理部は、前記誤差を重み付けした項を全期間の総報酬から減じた前記目的関数を最大化する請求項1または2に記載の情報処理装置。
- 前記処理部は、一の時点の各状態における施策の適用対象数に対し、当該一の時点の前の時点の各状態における施策の配分に応じた状態遷移によって前記一の時点および各状態に遷移してくる対象者数を算出して推定対象数とする請求項1から3のいずれか一項に記載の情報処理装置。
- 前記処理部は、各時点および各状態における施策の適用対象数の合計が予め定められた全対象数と等しくなる旨の制約条件を更に用いて、前記目的関数を最大化する請求項1から4のいずれか一項に記載の情報処理装置。
- 前記コスト制約取得部は、施策毎の合計コストを制約するコスト制約を取得する請求項1から5のいずれか一項に記載の情報処理装置。
- 複数の対象について施策に対する反応を記録した学習データを取得する学習データ取得部と、
前記学習データに基づいて、前記遷移モデルを生成するモデル生成部と、
を備える請求項1から6のいずれか一項に記載の情報処理装置。 - 前記モデル生成部は、
前記学習データに含まれる前記複数の対象を各状態に分類する分類部と、
各状態の対象が施策に応じてどの状態に遷移したかに基づいて、状態遷移確率を算出する算出部と、
を備える請求項7に記載の情報処理装置。 - 前記分類部は、
前記学習データに含まれる前記複数の対象のそれぞれに対する施策および反応に基づいて、当該対象の状態ベクトルを生成し、
前記状態ベクトルにより将来の報酬を回帰する際の予測精度が最大となる軸か、または前記状態ベクトルの分散が最大となる軸によって前記複数の対象を分類していくことにより、前記複数の対象を複数の状態に分類する
請求項8に記載の情報処理装置。 - 前記学習データに基づいて、対象の状態の遷移確率分布を算出する分布算出部と、
前記出力部が出力した各時点および各状態における施策の配分に応じて、前記遷移確率分布に基づく状態遷移をシミュレーションするシミュレーション部と、
を更に備える請求項7から9のいずれか一項に記載の情報処理装置。 - コンピュータにより実行される、各状態にある対象数が施策に応じて遷移する遷移モデルにおける施策を最適化する情報処理方法であって、
複数時点および複数状態の少なくとも一方に亘る施策の合計コストを制約するコスト制約を含む複数のコスト制約を取得するコスト制約取得段階と、
各時点および各状態における施策の配分を最適化対象の変数とし、各時点および各状態における施策の適用対象数と前記遷移モデルによる状態遷移に応じた各時点および各状態の推定対象数との間の誤差に応じた項を全期間の総報酬から減じた目的関数を、前記複数のコスト制約を満たしつつ最大化する処理段階と、
前記目的関数を最大化する各時点および各状態における施策の配分を出力する出力段階と、
を備える情報処理方法。 - コンピュータを各状態にある対象数が施策に応じて遷移する遷移モデルにおける施策を最適化する情報処理装置として機能させるプログラムであって、
実行されると当該コンピュータを、
複数時点および複数状態の少なくとも一方に亘る施策の合計コストを制約するコスト制約を含む複数のコスト制約を取得するコスト制約取得部と、
各時点および各状態における施策の配分を最適化対象の変数とし、各時点および各状態における施策の適用対象数と前記遷移モデルによる状態遷移に応じた各時点および各状態の推定対象数との間の誤差に応じた項を全期間の総報酬から減じた目的関数を、前記複数のコスト制約を満たしつつ最大化する処理部と、
前記目的関数を最大化する各時点および各状態における施策の配分を出力する出力部と、
して機能させるプログラム。
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JP2014067159A JP5963320B2 (ja) | 2014-03-27 | 2014-03-27 | 情報処理装置、情報処理方法、及び、プログラム |
US14/644,528 US20150278735A1 (en) | 2014-03-27 | 2015-03-11 | Information processing apparatus, information processing method and program |
US14/748,307 US20150294226A1 (en) | 2014-03-27 | 2015-06-24 | Information processing apparatus, information processing method and program |
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US20150316282A1 (en) * | 2014-05-05 | 2015-11-05 | Board Of Regents, The University Of Texas System | Strategy for efficiently utilizing a heat-pump based hvac system with an auxiliary heating system |
US10839302B2 (en) | 2015-11-24 | 2020-11-17 | The Research Foundation For The State University Of New York | Approximate value iteration with complex returns by bounding |
US10949492B2 (en) | 2016-07-14 | 2021-03-16 | International Business Machines Corporation | Calculating a solution for an objective function based on two objective functions |
US11368752B2 (en) | 2017-01-03 | 2022-06-21 | Bliss Point Media, Inc. | Optimization of broadcast event effectiveness |
WO2019018533A1 (en) * | 2017-07-18 | 2019-01-24 | Neubay Inc | NEURO-BAYESIAN ARCHITECTURE FOR THE IMPLEMENTATION OF GENERAL ARTIFICIAL INTELLIGENCE |
DE102021201918A1 (de) * | 2020-10-07 | 2022-04-07 | Robert Bosch Gesellschaft mit beschränkter Haftung | Vorrichtung und Verfahren zum Steuern ein oder mehrerer Roboter |
CN116529977A (zh) * | 2020-10-30 | 2023-08-01 | 西门子股份公司 | 分布式能源***的优化方法、装置和计算机可读存储介质 |
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JP3515267B2 (ja) * | 1996-02-29 | 2004-04-05 | 株式会社東芝 | 多層神経回路網学習装置 |
US6353814B1 (en) * | 1997-10-08 | 2002-03-05 | Michigan State University | Developmental learning machine and method |
US7451065B2 (en) * | 2002-03-11 | 2008-11-11 | International Business Machines Corporation | Method for constructing segmentation-based predictive models |
US7403904B2 (en) * | 2002-07-19 | 2008-07-22 | International Business Machines Corporation | System and method for sequential decision making for customer relationship management |
US20040204975A1 (en) * | 2003-04-14 | 2004-10-14 | Thomas Witting | Predicting marketing campaigns using customer-specific response probabilities and response values |
US20050071223A1 (en) * | 2003-09-30 | 2005-03-31 | Vivek Jain | Method, system and computer program product for dynamic marketing strategy development |
JP2006331390A (ja) * | 2005-05-28 | 2006-12-07 | Tepco Sysytems Corp | 大規模OnetoOneマーケティング向け最適キャンペーンを実施するためのモデル構築と求解実装方法 |
US8788306B2 (en) * | 2007-03-05 | 2014-07-22 | International Business Machines Corporation | Updating a forecast model |
US8516515B2 (en) * | 2007-04-03 | 2013-08-20 | Google Inc. | Impression based television advertising |
US7835937B1 (en) * | 2007-10-15 | 2010-11-16 | Aol Advertising Inc. | Methods for controlling an advertising campaign |
US20110231239A1 (en) * | 2010-03-16 | 2011-09-22 | Sharon Burt | Method and system for attributing an online conversion to multiple influencers |
US8484077B2 (en) * | 2010-07-21 | 2013-07-09 | Yahoo! Inc. | Using linear and log-linear model combinations for estimating probabilities of events |
US9117227B1 (en) * | 2011-03-31 | 2015-08-25 | Twitter, Inc. | Temporal features in a messaging platform |
US20130066665A1 (en) * | 2011-09-09 | 2013-03-14 | Deepali Tamhane | System and method for automated selection of workflows |
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