The Experts below are selected from a list of 294 Experts worldwide ranked by ideXlab platform
Lutz Maria Kolbe - One of the best experts on this subject based on the ideXlab platform.
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The Business Model Pattern Database: A Tool for Systematic BMI
Progress in IS, 2019Co-Authors: Gerrit Remane, Jan F. Tesch, Andre Hanelt, Lutz Maria KolbeAbstract:Companies are more frequently seen shifting their focus from technological innovation towards business model innovation. One efficient option for business model innovation is to learn from existing solutions, i.e., business model Patterns. However, the various understandings of the business model Pattern concept are often confusing and contradictory, with the available collections incomplete, overlapping, and inconsistently structured. Therefore, the rich body of literature on business model Patterns has not yet reached its full potential for both practical application as well as theoretic advancement. To help remedy this, we conduct an exhaustive review, filter for duplicates, and structure the Patterns along several dimensions by applying a rigorous taxonomy-building approach. The resulting business model Pattern Database allows for navigation to the relevant set of Patterns for a specific impact on a company’s business model. It can be used for systematic business model innovation, which we illustrate via a simplified case study.
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THE BUSINESS MODEL Pattern Database — A TOOL FOR SYSTEMATIC BUSINESS MODEL INNOVATION
International Journal of Innovation Management, 2017Co-Authors: Gerrit Remane, Jan F. Tesch, Lutz Maria KolbeAbstract:Companies are more frequently seen shifting their focus from technological innovation towards business model innovation. One efficient option for business model innovation is to learn from existing solutions, i.e., business model Patterns. However, the various understandings of the business model Pattern concept are often confusing and contradictory, with the available collections incomplete, overlapping, and inconsistently structured. Therefore, the rich body of literature on business model Patterns has not yet reached its full potential for both practical application as well as theoretic advancement. To help remedy this, we conduct an exhaustive review, filter for duplicates, and structure the Patterns along several dimensions by applying a rigorous taxonomy-building approach. The resulting business model Pattern Database allows for navigation to the relevant set of Patterns for a specific impact on a company’s business model. It can be used for systematic business model innovation, which we illustrate via a simplified case study.
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The Business Model Pattern Database-A Tool For Systematic Business Model Innovation
International Journal of Innovation Management, 2017Co-Authors: Gerrit Remane, Jan F. Tesch, Lutz Maria KolbeAbstract:Companies are more frequently seen shifting their focus from technological innovation towards business model innovation. One efficient option for business model innovation is to learn from existing solutions, i.e., business model Patterns. However, the various understandings of the business model Pattern concept are often confusing and contradictory, with the available collections incomplete, overlapping, and inconsistently structured. Therefore, the rich body of literature on business model Patterns has not yet reached its full potential for both practical application as well as theoretic advancement. To help remedy this, we conduct an exhaustive review, filter for duplicates, and structure the Patterns along several dimensions by applying a rigorous taxonomy-building approach. The resulting business model Pattern Database allows for navigation to the relevant set of Patterns for a specific impact on a company’s business model. It can be used for systematic business model innovation, which we illustrate via a simplified case study.
Gerrit Remane - One of the best experts on this subject based on the ideXlab platform.
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The Business Model Pattern Database: A Tool for Systematic BMI
Progress in IS, 2019Co-Authors: Gerrit Remane, Jan F. Tesch, Andre Hanelt, Lutz Maria KolbeAbstract:Companies are more frequently seen shifting their focus from technological innovation towards business model innovation. One efficient option for business model innovation is to learn from existing solutions, i.e., business model Patterns. However, the various understandings of the business model Pattern concept are often confusing and contradictory, with the available collections incomplete, overlapping, and inconsistently structured. Therefore, the rich body of literature on business model Patterns has not yet reached its full potential for both practical application as well as theoretic advancement. To help remedy this, we conduct an exhaustive review, filter for duplicates, and structure the Patterns along several dimensions by applying a rigorous taxonomy-building approach. The resulting business model Pattern Database allows for navigation to the relevant set of Patterns for a specific impact on a company’s business model. It can be used for systematic business model innovation, which we illustrate via a simplified case study.
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THE BUSINESS MODEL Pattern Database — A TOOL FOR SYSTEMATIC BUSINESS MODEL INNOVATION
International Journal of Innovation Management, 2017Co-Authors: Gerrit Remane, Jan F. Tesch, Lutz Maria KolbeAbstract:Companies are more frequently seen shifting their focus from technological innovation towards business model innovation. One efficient option for business model innovation is to learn from existing solutions, i.e., business model Patterns. However, the various understandings of the business model Pattern concept are often confusing and contradictory, with the available collections incomplete, overlapping, and inconsistently structured. Therefore, the rich body of literature on business model Patterns has not yet reached its full potential for both practical application as well as theoretic advancement. To help remedy this, we conduct an exhaustive review, filter for duplicates, and structure the Patterns along several dimensions by applying a rigorous taxonomy-building approach. The resulting business model Pattern Database allows for navigation to the relevant set of Patterns for a specific impact on a company’s business model. It can be used for systematic business model innovation, which we illustrate via a simplified case study.
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The Business Model Pattern Database-A Tool For Systematic Business Model Innovation
International Journal of Innovation Management, 2017Co-Authors: Gerrit Remane, Jan F. Tesch, Lutz Maria KolbeAbstract:Companies are more frequently seen shifting their focus from technological innovation towards business model innovation. One efficient option for business model innovation is to learn from existing solutions, i.e., business model Patterns. However, the various understandings of the business model Pattern concept are often confusing and contradictory, with the available collections incomplete, overlapping, and inconsistently structured. Therefore, the rich body of literature on business model Patterns has not yet reached its full potential for both practical application as well as theoretic advancement. To help remedy this, we conduct an exhaustive review, filter for duplicates, and structure the Patterns along several dimensions by applying a rigorous taxonomy-building approach. The resulting business model Pattern Database allows for navigation to the relevant set of Patterns for a specific impact on a company’s business model. It can be used for systematic business model innovation, which we illustrate via a simplified case study.
Ariel Felner - One of the best experts on this subject based on the ideXlab platform.
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ECAI - Compressing Pattern Databases with Learning
2008Co-Authors: Mehdi Samadi, Ariel Felner, Maryam Siabani, Robert C HolteAbstract:A Pattern Database (PDB) is a heuristic function implemented as a lookup table. It stores the lengths of optimal solutions for instances of subproblems. Most previous PDBs had a distinct entry in the table for each subproblem instance. In this paper we apply learning techniques to compress PDBs by using neural networks and decision trees thereby reducing the amount of memory needed. Experiments on the sliding tile puzzles and the TopSpin puzzle show that our compressed PDBs significantly outperforms both uncompressed PDBs as well as previous compressing methods. Our full compressing system reduced the size of memory needed by a factor of up to 63 at a cost of no more than a factor of 2 in the search effort.
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Compressed Pattern Databases
Journal of Artificial Intelligence Research, 2007Co-Authors: Ariel Felner, Richard E Korf, Ram Meshulam, Robert C HolteAbstract:A Pattern Database (PDB) is a heuristic function implemented as a lookup table that stores the lengths of optimal solutions for subproblem instances. Standard PDBs have a distinct entry in the table for each subproblem instance. In this paper we investigate compressing PDBs by merging several entries into one, thereby allowing the use of PDBs that exceed available memory in their uncompressed form. We introduce a number of methods for determining which entries to merge and discuss their relative merits. These vary from domain-independent approaches that allow any set of entries in the PDB to be merged, to more intelligent methods that take into account the structure of the problem. The choice of the best compression method is based on domain-dependent attributes. We present experimental results on a number of combinatorial problems, including the four-peg Towers of Hanoi problem, the sliding-tile puzzles, and the Top-Spin puzzle. For the Towers of Hanoi, we show that the search time can be reduced by up to three orders of magnitude by using compressed PDBs compared to uncompressed PDBs of the same size. More modest improvements were observed for the other domains.
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combining perimeter search and Pattern Database abstractions
Symposium on Abstraction Reformulation and Approximation, 2007Co-Authors: Ariel Felner, Nir OfekAbstract:A Pattern Database abstraction (PDB) is a heuristic function in a form of a lookup table. A PDB stores the cost of optimal solutions for instances of abstract problems (subproblems). These costs are used as admissible heuristics for the original problem. Perimeter search (PS) is a form of bidirectional search. First, a breadth-first search is performed backwards from the goal state. Then, a forward search is executed towards the nodes of the perimeter. In this paper we study the effect of combining these two techniques. We describe two methods for doing this. The simplified method uses a regular PDB (towards a single goal state) but uses the perimeter to correct heuristics of nodes outside the perimeter. The second, more advanced method is to build a PDB that stores the cost of reaching any node of the perimeter from a given Pattern. Although one might see great potential for speedup in the advanced method, we theoretically show that surprisingly most of the benefit of combining perimeter and PDBs is already exploited by the first method. We also provide experimental results that confirm our findings. We then study the behavior of our new approach when combined with methods for using multiple PDBs such as maxing and adding.
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IJCAI - Recent progress in heuristic search: a case study of the four-peg towers of Hanoi problem
2007Co-Authors: Richard E Korf, Ariel FelnerAbstract:We integrate a number of new and recent advances in heuristic search, and apply them to the fourpeg Towers of Hanoi problem. These include frontier search, disk-based search, parallel processing, multiple, compressed, disjoint, and additive Pattern Database heuristics, and breadth-first heuristic search. New ideas include Pattern Database heuristics based on multiple goal states, a method to reduce coordination among multiple parallel threads, and a method for reducing the number of heuristic calculations. We perform the first complete breadth-first searches of the 21 and 22-disc fourpeg Towers of Hanoi problems, and extend the verification of "presumed optimal solutions" to this problem from 24 to 30 discs. Verification of the 31-disc problem is in progress.
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Maximizing over multiple Pattern Databases speeds up heuristic search
Artificial Intelligence, 2006Co-Authors: Robert C Holte, Ariel Felner, Jack Newton, Ram Meshulam, David FurcyAbstract:AbstractA Pattern Database (PDB) is a heuristic function stored as a lookup table. This paper considers how best to use a fixed amount (m units) of memory for storing Pattern Databases. In particular, we examine whether using n Pattern Databases of size m/n instead of one Pattern Database of size m improves search performance. In all the state spaces considered, the use of multiple smaller Pattern Databases reduces the number of nodes generated by IDA*. The paper provides an explanation for this phenomenon based on the distribution of heuristic values that occur during search
Jan F. Tesch - One of the best experts on this subject based on the ideXlab platform.
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The Business Model Pattern Database: A Tool for Systematic BMI
Progress in IS, 2019Co-Authors: Gerrit Remane, Jan F. Tesch, Andre Hanelt, Lutz Maria KolbeAbstract:Companies are more frequently seen shifting their focus from technological innovation towards business model innovation. One efficient option for business model innovation is to learn from existing solutions, i.e., business model Patterns. However, the various understandings of the business model Pattern concept are often confusing and contradictory, with the available collections incomplete, overlapping, and inconsistently structured. Therefore, the rich body of literature on business model Patterns has not yet reached its full potential for both practical application as well as theoretic advancement. To help remedy this, we conduct an exhaustive review, filter for duplicates, and structure the Patterns along several dimensions by applying a rigorous taxonomy-building approach. The resulting business model Pattern Database allows for navigation to the relevant set of Patterns for a specific impact on a company’s business model. It can be used for systematic business model innovation, which we illustrate via a simplified case study.
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THE BUSINESS MODEL Pattern Database — A TOOL FOR SYSTEMATIC BUSINESS MODEL INNOVATION
International Journal of Innovation Management, 2017Co-Authors: Gerrit Remane, Jan F. Tesch, Lutz Maria KolbeAbstract:Companies are more frequently seen shifting their focus from technological innovation towards business model innovation. One efficient option for business model innovation is to learn from existing solutions, i.e., business model Patterns. However, the various understandings of the business model Pattern concept are often confusing and contradictory, with the available collections incomplete, overlapping, and inconsistently structured. Therefore, the rich body of literature on business model Patterns has not yet reached its full potential for both practical application as well as theoretic advancement. To help remedy this, we conduct an exhaustive review, filter for duplicates, and structure the Patterns along several dimensions by applying a rigorous taxonomy-building approach. The resulting business model Pattern Database allows for navigation to the relevant set of Patterns for a specific impact on a company’s business model. It can be used for systematic business model innovation, which we illustrate via a simplified case study.
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The Business Model Pattern Database-A Tool For Systematic Business Model Innovation
International Journal of Innovation Management, 2017Co-Authors: Gerrit Remane, Jan F. Tesch, Lutz Maria KolbeAbstract:Companies are more frequently seen shifting their focus from technological innovation towards business model innovation. One efficient option for business model innovation is to learn from existing solutions, i.e., business model Patterns. However, the various understandings of the business model Pattern concept are often confusing and contradictory, with the available collections incomplete, overlapping, and inconsistently structured. Therefore, the rich body of literature on business model Patterns has not yet reached its full potential for both practical application as well as theoretic advancement. To help remedy this, we conduct an exhaustive review, filter for duplicates, and structure the Patterns along several dimensions by applying a rigorous taxonomy-building approach. The resulting business model Pattern Database allows for navigation to the relevant set of Patterns for a specific impact on a company’s business model. It can be used for systematic business model innovation, which we illustrate via a simplified case study.
Pascal Bercher - One of the best experts on this subject based on the ideXlab platform.
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Pattern Database heuristics for fully observable nondeterministic planning
International Conference on Automated Planning and Scheduling, 2010Co-Authors: Robert Mattmuller, Manuela Ortlieb, Malte Helmert, Pascal BercherAbstract:When planning in an uncertain environment, one is often interested in finding a contingent plan that prescribes appropriate actions for all possible states that may be encountered during the execution of the plan. We consider the problem of finding strong cyclic plans for fully observable nondeterministic (FOND) planning problems. The algorithm we choose is LAO*, an informed explicit state search algorithm. We investigate the use of Pattern Database (PDB) heuristics to guide LAO* towards goal states. To obtain a fully domain-independent planning system, we use an automatic Pattern selection procedure that performs local search in the space of Pattern collections. The evaluation of our system on the FOND benchmarks of the Uncertainty Part of the International Planning Competition 2008 shows that our approach is competitive with symbolic regression search in terms of problem coverage, speed, and plan quality.
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ICAPS - Pattern Database heuristics for fully observable nondeterministic planning
2010Co-Authors: Robert Mattmuller, Manuela Ortlieb, Malte Helmert, Pascal BercherAbstract:When planning in an uncertain environment, one is often interested in finding a contingent plan that prescribes appropriate actions for all possible states that may be encountered during the execution of the plan. We consider the problem of finding strong cyclic plans for fully observable nondeterministic (FOND) planning problems. The algorithm we choose is LAO*, an informed explicit state search algorithm. We investigate the use of Pattern Database (PDB) heuristics to guide LAO* towards goal states. To obtain a fully domain-independent planning system, we use an automatic Pattern selection procedure that performs local search in the space of Pattern collections. The evaluation of our system on the FOND benchmarks of the Uncertainty Part of the International Planning Competition 2008 shows that our approach is competitive with symbolic regression search in terms of problem coverage, speed, and plan quality.
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KI - Solving non-deterministic planning problems with Pattern Database heuristics
KI 2009: Advances in Artificial Intelligence, 2009Co-Authors: Pascal Bercher, Robert MattmullerAbstract:Non-determinism arises naturally in many real-world applications of action planning. Strong plans for this type of problems can be found using AO* search guided by an appropriate heuristic function. Most domain-independent heuristics considered in this context so far are based on the idea of ignoring delete lists and do not properly take the non-determinism into account. Therefore, we investigate the applicability of Pattern Database (PDB) heuristics to nondeterministic planning. PDB heuristics have emerged as rather informative in a deterministic context. Our empirical results suggest that PDB heuristics can also perform reasonably well in non-deterministic planning. Additionally, we present a generalization of the Pattern additivity criterion known from classical planning to the non-deterministic setting.
