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Optimal multivariate decision trees Constraints (IF 1.6) Pub Date : 2023-12-27 Justin Boutilier, Carla Michini, Zachary Zhou
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A feature commonality-based search strategy to find high $$t$$ -wise covering solutions in feature models Constraints (IF 1.6) Pub Date : 2023-11-30 Mathieu Vavrille
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Learning to select SAT encodings for pseudo-Boolean and linear integer constraints Constraints (IF 1.6) Pub Date : 2023-11-02 Felix Ulrich-Oltean, Peter Nightingale, James Alfred Walker
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Reasoning and inference for (Maximum) satisfiability: new insights Constraints (IF 1.6) Pub Date : 2023-10-23 Mohamed Sami Cherif
At the heart of computer science and artificial intelligence, logic is often used as a powerful language to model and solve complex problems that arise in academia and in real-world applications. A well-known formalism in this context is the Satisfiability (SAT) problem which simply checks whether a given propositional formula in the form of a set of constraints, called clauses, can be satisfied. A
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CSP beyond tractable constraint languages Constraints (IF 1.6) Pub Date : 2023-10-11 Jan Dreier, Sebastian Ordyniak, Stefan Szeider
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Learn and route: learning implicit preferences for vehicle routing Constraints (IF 1.6) Pub Date : 2023-10-11 Rocsildes Canoy, Víctor Bucarey, Jayanta Mandi, Tias Guns
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Complexity of minimum-size arc-inconsistency explanations Constraints (IF 1.6) Pub Date : 2023-10-02 Christian Bessiere, Clément Carbonnel, Martin C. Cooper, Emmanuel Hebrard
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Spacetime programming: a synchronous language for constraint search Constraints (IF 1.6) Pub Date : 2023-09-23 Pierre Talbot
Constraint programming is a paradigm for computing with mathematical relations named constraints. It is a declarative approach to describe many real-world problems including scheduling, vehicles routing, biology and musical composition. Constraint programming must be contrasted with procedural approaches that describe how a problem is solved, whereas constraint models describe what the problem is.
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Optimization methods based on decision diagrams for constraint programming, AI planning, and mathematical programming Constraints (IF 1.6) Pub Date : 2023-09-22 Margarita Paz Castro
Decision diagrams (DDs) are graphical structures that can be used to solve discrete optimization problems by representing the set of feasible solutions as paths in a graph. This graphical encoding of the feasibility set can represent complex combinatorial structures and is the foundation of several novel optimization techniques. Due to their flexibility, DDs have become an attractive optimization tool
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Constraint programming approaches to electric vehicle and robot routing problems Constraints (IF 1.6) Pub Date : 2023-09-22 Kyle E. C. Booth
Driven by global efforts to curb greenhouse gas emissions, there has been significant investment in electric vehicle (EV) technology in recent years, resulting in a substantial increase in EV market share. Concurrently, the demand for mobile robots, such as unmanned aerial vehicles (UAVs) and land-based robots, has also experienced rapid growth, encouraged by recent advances in the autonomy and capabilities
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Scheduling through logic-based tools Constraints (IF 1.6) Pub Date : 2023-09-18 Jordi Coll Caballero
A scheduling problem can be defined in a nutshell as the problem of determining when and how the activities of a project have to be run, according to some project requirements. Such problems are ubiquitous nowadays since they frequently appear in industry and services. In most cases the computation of solutions of scheduling problems is hard, especially when some objective, such as the duration of
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Floating-point numbers round-off error analysis by constraint programming Constraints (IF 1.6) Pub Date : 2023-09-15 Rémy Garcia
Floating-point numbers are used in many applications to perform computations, often without the user’s knowledge. The mathematical models of these applications use real numbers that are often not representable on a computer. Indeed, a finite binary representation is not sufficient to represent the continuous and infinite set of real numbers. The problem is that computing with floating-point numbers
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Security-Aware Database Migration Planning Constraints (IF 1.6) Pub Date : 2023-08-10 Utku Umur Acikalin, Bugra Caskurlu, K. Subramani
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The extensional constraint Constraints (IF 1.6) Pub Date : 2023-08-03 Hélène Verhaeghe
Extensional constraints are crucial in CP. They represent allowed combinations of values for a subset of variables (scope) using extensional representation forms such as tables (lists of tuples of constraint solutions) or MDDs (layered acyclic directed graphs where each path represents a constraint solution). Such extensional forms allow the modelization of virtually any kind of constraints. This type
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Activity propagation in systems of linear inequalities and its relation to block-coordinate descent in linear programs Constraints (IF 1.6) Pub Date : 2023-07-25 Tomáš Dlask, Tomáš Werner
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Human-centred feasibility restoration in practice Constraints (IF 1.6) Pub Date : 2023-07-20 Ilankaikone Senthooran, Matthias Klapperstueck, Gleb Belov, Tobias Czauderna, Kevin Leo, Mark Wallace, Michael Wybrow, Maria Garcia de la Banda
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A constraint programming model for the scheduling and workspace layout design of a dual-arm multi-tool assembly robot Constraints (IF 1.6) Pub Date : 2023-07-17 Johan Wessén, Mats Carlsson, Christian Schulte, Pierre Flener, Federico Pecora, Mihhail Matskin
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Hybrid optimization of vehicle routing problems Constraints (IF 1.6) Pub Date : 2023-07-17 Edward Lam
Vehicle routing problems are combinatorial optimization problems that aspire to design vehicle routes that minimize some measure of cost, such as the total distance traveled or the time at which the last vehicle returns to a depot, while adhering to various restrictions. Vehicle routing problems are of profound interest in both academia and industry because they are opportunities to study graph structures
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Block-coordinate descent and local consistencies in linear programming Constraints (IF 1.6) Pub Date : 2023-07-08 Tomáš Dlask
Even though linear programming (LP) problems can be solved in polynomial time, solving large-scale LP instances using off-the-shelf solvers may be difficult in practice, which creates demand for specialized scalable methods. One such method for large-scale problems is block-coordinate descent (BCD). However, the fixed points of this method need not be global optima even for convex optimization problems
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SAT-based optimal classification trees for non-binary data Constraints (IF 1.6) Pub Date : 2023-07-08 Pouya Shati, Eldan Cohen, Sheila A. McIlraith
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Exact methods for the Oven Scheduling Problem Constraints (IF 1.6) Pub Date : 2023-07-04 Marie-Louise Lackner, Christoph Mrkvicka, Nysret Musliu, Daniel Walkiewicz, Felix Winter
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Computing relaxations for the three-dimensional stable matching problem with cyclic preferences Constraints (IF 1.6) Pub Date : 2023-06-03 Ágnes Cseh, Guillaume Escamocher, Luis Quesada
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Super-reparametrizations of weighted CSPs: properties and optimization perspective Constraints (IF 1.6) Pub Date : 2023-05-16 Tomáš Dlask, Tomáš Werner, Simon de Givry
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The smallest hard trees Constraints (IF 1.6) Pub Date : 2023-03-25 Manuel Bodirsky, Jakub Bulín, Florian Starke, Michael Wernthaler
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An interdisciplinary experimental evaluation on the disjunctive temporal problem Constraints (IF 1.6) Pub Date : 2023-02-01 Matteo Zavatteri, Alice Raffaele, Dario Ostuni, Romeo Rizzi
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Towards better heuristics for solving bounded model checking problems Constraints (IF 1.6) Pub Date : 2022-12-27 Anissa Kheireddine, Etienne Renault, Souheib Baarir
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The algebraic structure of the densification and the sparsification tasks for CSPs Constraints (IF 1.6) Pub Date : 2022-12-08 Rustem Takhanov
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An algorithm-independent measure of progress for linear constraint propagation Constraints (IF 1.6) Pub Date : 2022-10-12 Boro Sofranac, Ambros Gleixner, Sebastian Pokutta
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Short- and medium-term optimization of underground mine planning using constraint programming Constraints (IF 1.6) Pub Date : 2022-09-27 Louis-Pierre Campeau, Michel Gamache
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Solution sampling with random table constraints Constraints (IF 1.6) Pub Date : 2022-06-24 Mathieu Vavrille, Charlotte Truchet, Charles Prud’homme
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Boosting isomorphic model filtering with invariants Constraints (IF 1.6) Pub Date : 2022-06-16 João Araújo, Choiwah Chow, Mikoláš Janota
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Propagation complete encodings of smooth DNNF theories Constraints (IF 1.6) Pub Date : 2022-06-03 Petr Kučera, Petr Savický
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Fast and parallel decomposition of constraint satisfaction problems Constraints (IF 1.6) Pub Date : 2022-06-03 Georg Gottlob, Cem Okulmus, Reinhard Pichler
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A collection of Constraint Programming models for the three-dimensional stable matching problem with cyclic preferences Constraints (IF 1.6) Pub Date : 2022-06-01 Ágnes Cseh, Guillaume Escamocher, Begüm Genç, Luis Quesada
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How constraint programming can help chemists to generate Benzenoid structures and assess the local Aromaticity of Benzenoids Constraints (IF 1.6) Pub Date : 2022-05-28 Yannick Carissan, Denis Hagebaum-Reignier, Nicolas Prcovic, Cyril Terrioux, Adrien Varet
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Generative magic and designing magic performances with constraint programming Constraints (IF 1.6) Pub Date : 2022-05-21 Guilherme de Azevedo Silveira
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When bounds consistency implies domain consistency for regular counting constraints Constraints (IF 1.6) Pub Date : 2022-05-20 Barnaby Martin, Justin Pearson
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A constraint-based approach to learn temporal features on action models from multiple plans Constraints (IF 1.6) Pub Date : 2022-05-12 Antonio Garrido
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Learning the travelling salesperson problem requires rethinking generalization Constraints (IF 1.6) Pub Date : 2022-04-28 Chaitanya K. Joshi, Quentin Cappart, Louis-Martin Rousseau, Thomas Laurent
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Concise integer linear programming formulation for clique partitioning problems Constraints (IF 1.6) Pub Date : 2022-04-23 Miyuki Koshimura, Emi Watanabe, Yuko Sakurai, Makoto Yokoo
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Correct approximation of IEEE 754 floating-point arithmetic for program verification Constraints (IF 1.6) Pub Date : 2022-02-22 Roberto Bagnara, Abramo Bagnara, Fabio Biselli, Michele Chiari, Roberta Gori
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Complete symmetry breaking constraints for the class of uniquely Hamiltonian graphs Constraints (IF 1.6) Pub Date : 2022-01-25 Avraham Itzhakov, Michael Codish
This paper introduces, for the first time, a complete symmetry breaking constraint of polynomial size for a significant class of graphs: the class of uniquely Hamiltonian graphs. We introduce a canonical form for uniquely Hamiltonian graphs and prove that testing whether a given uniquely Hamiltonian graph is canonical can be performed efficiently. Based on this canonicity test, we construct a complete
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Global domain views for expressive and cross-domain constraint programming Constraints (IF 1.6) Pub Date : 2022-01-11 Dimitri Justeau-Allaire, Charles Prud’homme
The concept of domain views is a powerful abstraction in constraint programming. It permits to define variables that do not declare any domain but instead rely on a variable x and a function f, such that \(y = f(x)\) where y is the view. In addition to making modelling easier by providing an expressive layer of abstraction, views provide an alternative to constraint decomposition that does not involve
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Variable ordering for decision diagrams: A portfolio approach Constraints (IF 1.6) Pub Date : 2022-01-04 Anthony Karahalios, Willem-Jan van Hoeve
Relaxed decision diagrams have been successfully applied to solve combinatorial optimization problems, but their performance is known to strongly depend on the variable ordering. We propose a portfolio approach to selecting the best ordering among a set of alternatives. We consider several different portfolio mechanisms: a static uniform time-sharing portfolio, an offline predictive model of the single
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Cable tree wiring - benchmarking solvers on a real-world scheduling problem with a variety of precedence constraints Constraints (IF 1.6) Pub Date : 2021-06-15 Jana Koehler, Josef Bürgler, Urs Fontana, Etienne Fux, Florian Herzog, Marc Pouly, Sophia Saller, Anastasia Salyaeva, Peter Scheiblechner, Kai Waelti
Cable trees are used in industrial products to transmit energy and information between different product parts. To this date, they are mostly assembled by humans and only few automated manufacturing solutions exist using complex robotic machines. For these machines, the wiring plan has to be translated into a wiring sequence of cable plugging operations to be followed by the machine. In this paper
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Correction to: The potential of quantum annealing for rapid solution structure identification Constraints (IF 1.6) Pub Date : 2021-04-12 Yuchen Pang, Carleton Coffrin, Andrey Y. Lokhov, Marc Vuffray
A Correction to this paper has been published: https://doi.org/10.1007/s10601-021-09320-x
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Learn to relax: Integrating 0-1 integer linear programming with pseudo-Boolean conflict-driven search Constraints (IF 1.6) Pub Date : 2021-01-18 Jo Devriendt, Ambros Gleixner, Jakob Nordström
Conflict-driven pseudo-Boolean solvers optimize 0-1 integer linear programs by extending the conflict-driven clause learning (CDCL) paradigm from SAT solving. Though pseudo-Boolean solvers have the potential to be exponentially more efficient than CDCL solvers in theory, in practice they can sometimes get hopelessly stuck even when the linear programming (LP) relaxation is infeasible over the reals
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Introduction to the CPAIOR 2020 fast track issue Constraints (IF 1.6) Pub Date : 2020-12-01 Emmanuel Hebrard,Nysret Musliu
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The potential of quantum annealing for rapid solution structure identification Constraints (IF 1.6) Pub Date : 2020-11-18 Yuchen Pang, Carleton Coffrin, Andrey Y. Lokhov, Marc Vuffray
The recent emergence of novel computational devices, such as quantum computers, coherent Ising machines, and digital annealers presents new opportunities for hardware-accelerated hybrid optimization algorithms. Unfortunately, demonstrations of unquestionable performance gains leveraging novel hardware platforms have faced significant obstacles. One key challenge is understanding the algorithmic properties
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Power of pre-processing: production scheduling with variable energy pricing and power-saving states Constraints (IF 1.6) Pub Date : 2020-11-16 Ondřej Benedikt, István Módos, Zdeněk Hanzálek
This paper addresses a single machine scheduling problem with non-preemptive jobs to minimize the total electricity cost. Two latest trends in the area of the energy-aware scheduling are considered, namely the variable energy pricing and the power-saving states of a machine. Scheduling of the jobs and the machine states are considered jointly to achieve the highest possible savings. Although this problem
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Exact and flexible solution approach to a critical chain project management problem Constraints (IF 1.6) Pub Date : 2020-11-10 Hiroyuki Goto, Alan T. Murray
An important component in critical chain project management is a scheduling methodology to support resource constraints. Given task duration estimates and precedence relations along with resource designation for each task, one seeks to adhere to a target due date as well as to shorten the estimated makespan. Key are two types of time buffers to absorb potential delays accumulated from preceding tasks
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A new constraint programming model and solving for the cyclic hoist scheduling problem Constraints (IF 1.6) Pub Date : 2020-11-09 Mark Wallace, Neil Yorke-Smith
The cyclic hoist scheduling problem (CHSP) is a well-studied optimisation problem due to its importance in industry. Despite the wide range of solving techniques applied to the CHSP and its variants, the models have remained complicated and inflexible, or have failed to scale up with larger problem instances. This article re-examines modelling of the CHSP and proposes a new simple, flexible constraint
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On certifying the UNSAT result of dynamic symmetry-handling-based SAT solvers Constraints (IF 1.6) Pub Date : 2020-10-30 Rodrigue Konan Tchinda, Clémentin Tayou Djamegni
SAT solvers are nowadays used in many applications where the UNSAT result has a special meaning that is at time critical. SAT instances sometimes exhibit symmetries which can be exploited to produce short proofs that would have been exponential for resolution alone. However, current unsatisfiability proof formats do not support symmetrical learning on which dynamic symmetry handling is based. We present
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Learning optimal decision trees using constraint programming Constraints (IF 1.6) Pub Date : 2020-10-29 Hélène Verhaeghe, Siegfried Nijssen, Gilles Pesant, Claude-Guy Quimper, Pierre Schaus
Decision trees are among the most popular classification models in machine learning. Traditionally, they are learned using greedy algorithms. However, such algorithms pose several disadvantages: it is difficult to limit the size of the decision trees while maintaining a good classification accuracy, and it is hard to impose additional constraints on the models that are learned. For these reasons, there
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Efficient multiple constraint acquisition Constraints (IF 1.6) Pub Date : 2020-09-17 Dimosthenis C. Tsouros, Kostas Stergiou
Constraint acquisition systems such as QuAcq and MultiAcq can assist non-expert users to model their problems as constraint networks by classifying (partial) examples as positive or negative. For each negative example, the former focuses on one constraint of the target network, while the latter can learn a maximum number of constraints. Two bottlenecks of the acquisition process where both these algorithms
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The flexible and real-time commute trip sharing problems Constraints (IF 1.6) Pub Date : 2020-08-19 Mohd. Hafiz Hasan, Pascal Van Hentenryck
The Commute Trip Sharing Problem (CTSP) was introduced to remove parking pressure on cities, as well as corporate and university campuses. Its goal is to reduce the number of vehicles being used for daily commuting activities. Given a set of inbound and outbound requests, which consists of origin and destination pairs and their departure and return times, the CTSP assigns riders and a driver, as well
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Non-local configuration of component interfaces by constraint satisfaction Constraints (IF 1.6) Pub Date : 2020-08-05 Olga Tveretina, Pavel Zaichenkov, Alex Shafarenko
Service-oriented computing is the paradigm that utilises services as fundamental elements for developing applications. Service composition, where data consistency becomes especially important, is still a key challenge for service-oriented computing. We maintain that there is one aspect of Web service communication on the data conformance side that has so far escaped the researchers attention. Aggregation
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Invariants for time-series constraints Constraints (IF 1.6) Pub Date : 2020-07-18 Ekaterina Arafailova, Nicolas Beldiceanu, Helmut Simonis
Many constraints restricting the result of some computations over an integer sequence can be compactly represented by counter automata. We improve the propagation of the conjunction of such constraints on the same sequence by synthesising a database of linear and non-linear invariants using their counter-automaton representation. The obtained invariants are formulae parameterised by the sequence length
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XCSP 3 and its ecosystem Constraints (IF 1.6) Pub Date : 2020-02-06 Gilles Audemard, Frédéric Boussemart, Christophe Lecoutre, Cédric Piette, Olivier Roussel
In this paper, we present a summary of XCSP3, together with its ecosystem. XCSP3 is a format used to build integrated representations of combinatorial constrained problems. Interestingly, XCSP3 preserves the structure of models, by handling arrays of variables and groups/blocks of constraints, which makes it rather unique in the literature. Furthermore, the ecosystem of XCSP3 is well supplied: it includes
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Integrated integer programming and decision diagram search tree with an application to the maximum independent set problem Constraints (IF 1.6) Pub Date : 2020-01-15 Jaime E. González, Andre A. Cire, Andrea Lodi, Louis-Martin Rousseau
We propose an optimization framework which integrates decision diagrams (DDs) and integer linear programming (ILP) to solve combinatorial optimization problems. The hybrid DD-ILP approach explores the solution space based on a recursive compilation of relaxed DDs and incorporates ILP calls to solve subproblems associated with DD nodes. The selection of DD nodes to be explored by ILP technology is a