Planning through Automatic Portfolio Configuration: The PbP Approach

In the field of domain-independent planning, several powerful planners implementing different techniques have been developed. However, no one of these systems outperforms all others in every known benchmark domain. In this work, we propose a multi-planner approach that automatically configures a portfolio of planning techniques for each given domain. The configuration process for a given domain uses a set of training instances to: (i) compute and analyze some alternative sets of macro-actions for each planner in the portfolio identifying a (possibly empty) useful set, (ii) select a cluster of planners, each one with the identified useful set of macro-actions, that is expected to perform best, and (iii) derive some additional information for configuring the execution scheduling of the selected planners at planning time. The resulting planning system, called PbP (Portfolio- based Planner), has two variants focusing on speed and plan quality. Different versions of PbP entered and won the learning track of the sixth and seventh International Planning Competitions. In this paper, we experimentally analyze PbP considering planning speed and plan quality in depth. We provide a collection of results that help to understand PbP�s behavior, and demonstrate the effectiveness of our approach to configuring a portfolio of planners with macro-actions

Identifying and Exploiting Features for Effective Plan Retrieval in Case-Based Planning

Case-Based planning can fruitfully exploit knowledge gained by solving a large number of problems, storing the corresponding solutions in a plan library and reusing them for solving similar planning problems in the future. Case-based planning is extremely effective when similar reuse candidates can be efficiently chosen. In this paper, we study an innovative technique based on planning problem features for efficiently retrieving solved planning problems (and relative plans) from large plan libraries. A problem feature is a characteristic of the instance that can be automatically derived from the problem specification, domain and search space analyses, and different problem encodings. Since the use of existing planning features are not always able to effectively distinguish between problems within the same planning domain, we introduce a new class of features. An experimental analysis in this paper shows that our features-based retrieval approach can significantly improve the performance of a state-of-the-art case-based planning system

Empirical observations and numerical modelling of tides, channel morphology, and vegetative effects on accretion in a restored tidal marsh

Tidal marshes form at the confluence between estuarine and marine environments where tidal movement regulates their developmental processes. Here, we investigate how the interplay between tides, channel morphology, and vegetation affect sediment dynamics in a low energy tidal marsh at the Paul S. Sarbanes Ecosystem Restoration Project at Poplar Island. Poplar Island is an active restoration site where fine‐grained material dredged from navigation channels in the upper Chesapeake Bay are being used to restore remote tidal marsh habitat toward the middle bay (Maryland, USA). Tidal currents were measured over multiple tidal cycles in the inlets and tidal creeks of one marsh at Poplar Island, Cell 1B, using Acoustic Doppler Current Profilers (ADCP) to estimate water fluxes throughout the marsh complex. Sediment fluxes were estimated using acoustic backscatter recorded by ADCPs and validated against total suspended solid measurements taken on site. A high‐resolution geomorphic survey was conducted to capture channel cross sections and tidal marsh morphology. We integrated simple numerical models built in Delft3d with empirical observations to identify which eco‐geomorphological factors influence sediment distribution in various channel configurations with differing vegetative characteristics. Channel morphology influences flood‐ebb dominance in marshes, where deep, narrow channels promote high tidal velocities and incision, increasing sediment suspension and reducing resilience in marshes at Poplar Island. Our numerical models suggest that accurately modelling plant phenology is vital for estimating sediment accretion rates. In‐situ observations indicate that Poplar Island marshes are experiencing erosion typical for many Chesapeake Bay islands. Peak periods of sediment suspension frequently coincide with the largest outflows of water during ebb tides resulting in large sediment deficits. Ebb dominance (net sediment export) in tidal marshes is likely amplified by sea‐level rise and may lower marsh resilience. We couple field observations with numerical models to understand how tidal marsh morphodynamics contribute to marsh resilience

わたしの好きな絵

若冲の鸚鵡図 / 伊藤達也レオン・スピリアールト「めまい」 / エリス俊子ユディトとクリムト / 太田惠雄私の好きな絵 ―モネ「ロンドン国会議事堂」 / 岡田新陶芸家バーナードリーチのデッサン画 / 加藤滋伸眩しすぎる月明かり / 亀山郁夫麦をふるう女たち / 木内尭『戦艦テメレール号』と共に見る遷り変り / 後藤希望イギリスで議会制民主主義を思う / 小堀慎悟Sgt. Pepper’s Lonely Hearts Club Band / 佐藤雄大アッシジの聖フランチェスコ / 白井史人オランダ人画家が「創造」した南アメリカの風景 / 鈴木茂「サン・ベルナール峠を越えるボナパルト」 / 竹下裕隆チュルリョーニスの楽園 / 沼野充義結核をどのように表象し想像するか / 福田眞人「私が好きな絵」を描くこと / 真崎翔「おまえの口に口づけしたよ、ヨカナーン」 / ムーディ美穂「TAKESHIMA」（二〇〇九）愛知県蒲郡市 / 吉見かおるUne image que j’aime de Simone de Beauvoir : Brassaï (1944). Simone de Beauvoir, café de Flore, Paris. Photographie, épreuve contact aux sels d’argent (8,5 x 5,9), BNF. / Anne-Claire CASSIUSクリムトのお休み前の『接吻』 / Alessandro G. GereviniArt is my GPS / Camilo VillanuevaThe Young Lady with the Shiner / Mathew Whit

Exploiting Macro-actions and Predicting Plan Length in Planning as Satisfiability

The use of automatically learned knowledge for a planning domain can significantly improve the performance of a generic planner when solving a problem in this domain. In this work, we focus on the well-known SAT-based approach to planning and investigate two types of learned knowledge that have not been studied in this planning framework before: macro-actions and planning horizon. Macro-actions are sequences of actions that typically occur in the solution plans, while a planning horizon of a problem is the length of a (possibly optimal) plan solving it. We propose a method that uses a machine learning tool for building a predictive model of the optimal planning horizon, and variants of the well-known planner SatPlan and solver MiniSat that can exploit macro actions and learned planning horizons to improve their performance. An experimental analysis illustrates the effectiveness of the proposed techniques

PbP2: Automatic Configuration of a Portfolio-based Multi-Planner

We present PbP2, an automated system that generates efficient domain-specific multi planners from a portfolio of domain-independent planning techniques by (i) computing some sets of macro-actions for every planner in the portfolio, (ii) optimizing the parameter setting of the parameterized planners in the portfolio, (iii) selecting a promising combination of planners in the portfolio and relative useful macro-actions, and (iv) defining some running time slots for their round-robin scheduling during planning. The configuration of the portfolio yielding the multi planner relies on some knowledge about the performance of the planners and relative macro actions, which is automatically generated from a training problem set. PbP2 is a revision and extension of a preliminary version of this system (PbP) that was awarded at the learning track of IPC-2008