This competition paper presents microPhantom, a bot playing microRTS and participating in the 2020 microRTS AI competition. microPhantom is based on our previous bot POAdaptive which won the partially observable track...
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ISBN:
(纸本)9781728145334
This competition paper presents microPhantom, a bot playing microRTS and participating in the 2020 microRTS AI competition. microPhantom is based on our previous bot POAdaptive which won the partially observable track of the 2018 and 2019 microRTS AI competitions. In this paper, we focus on decision-making under uncertainty, by tackling the Unit Production Problem with a method based on a combination of constraint programming and decision theory. We show that using our method to decide which units to train improves significantly the win rate against the second-best microRTS bot from the partially observable track. We also show that our method is resilient in chaotic environments, with a very small loss of efficiency only. To allow replicability and to facilitate further research, the source code of microPhantom is available, as well as the constraint programming toolkit it uses.
The article considers ontology as a set of relations (unary and binary) which are represented by specialized matrix like structures C-systems. That allows us to consider tasks of inference on ontologies as constraint ...
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ISBN:
(纸本)9781643680453;9781643680446
The article considers ontology as a set of relations (unary and binary) which are represented by specialized matrix like structures C-systems. That allows us to consider tasks of inference on ontologies as constraint satisfaction problems. A method of a priori analysis and transformation of SPARQL queries patterns into a form which speeds up the subsequent execution of concrete user queries has been developed. The method is oriented on ontologies developed with using content ontology design patterns, that ensures the predictability of the structure of potential queries. The method is based on the combining of the methods of structural decomposition and the original methods for non-numerical constraints satisfaction.
In this article, we consider the problem of planning maintenance operations at a locomotive maintenance depot. There are three types of tracks at the depot: buffer tracks, access tracks and service tracks. A depot con...
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ISBN:
(纸本)9783030386030;9783030386023
In this article, we consider the problem of planning maintenance operations at a locomotive maintenance depot. There are three types of tracks at the depot: buffer tracks, access tracks and service tracks. A depot consists of up to one buffer track and a number of access tracks, each of them ending with one service track. Each of these tracks has a limited capacity measured in locomotive sections. We present a constraint programming model and a greedy algorithm for solving the problem of planning maintenance operations. Using lifelike data based on the operation of several locomotive maintenance depots in Eastern polygon of Russian Railways, we carry out numerical experiments to compare the presented approaches.
A mixed model assembly line is production line where various product models are assembled. Line balancing and model sequencing problems are important for the efficiency of the assembly line. This paper solves them sim...
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A mixed model assembly line is production line where various product models are assembled. Line balancing and model sequencing problems are important for the efficiency of the assembly line. This paper solves them simultaneously aiming to minimize the latest completion time. A mixed integer liner programming model and a constraint programming model are proposed to provide the exact solution of the problem with station‐dependent assembly times. Because of NP‐hardness, a variable neighborhood simulated annealing algorithm is applied and compared to the hybrid simulated annealing algorithm from the literature. To strength the search process, a encoding method and a decoding method were proposed. Numerical results statistically show the efficiency of the proposed algorithm in terms of both the quality of solution and the time of achieving the best solution.
Answer set programming is a leading declarative constraint programming paradigm with wide use for complex knowledge-intensive applications. Modern answer set programming languages support many equivalent ways to model...
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Answer set programming is a leading declarative constraint programming paradigm with wide use for complex knowledge-intensive applications. Modern answer set programming languages support many equivalent ways to model constraints and specifications in a program. However, so far answer set programming has failed to develop systematic methodologies for building representations that would uniformly lend well to automated processing. This suggests that encoding selection, in the same way as algorithm selection and portfolio solving, may be a viable direction for improving performance of answer-set solving. The necessary precondition is automating the process of generating possible alternative encodings. Here we present an automated rewriting system, the Automated Aggregator or AAgg, that given a non-ground logic program, produces a family of equivalent programs with complementary performance when run under modern answer set programming solvers. We demonstrate this behavior through experimental analysis and propose the system's use in automated answer set programming solver selection tools.
The Traveling Salesperson Problem (TSP) is one of the bestknown problems in computer science. The Euclidean TSP is a special case in which each node is identified by its coordinates on the plane and the Euclidean dist...
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ISBN:
(纸本)9781577358350
The Traveling Salesperson Problem (TSP) is one of the bestknown problems in computer science. The Euclidean TSP is a special case in which each node is identified by its coordinates on the plane and the Euclidean distance is used as cost function. Many works in the constraint programming (CP) literature addressed the TSP, and use as benchmark Euclidean instances;however the usual approach is to build a distance matrix from the points coordinates, and then address the problem as a TSP, disregarding the information carried by the points coordinates for constraint propagation. In this work, we propose to use geometric information. present in Euclidean TSP instances, to improve the filtering power. In order to have a declarative approach, we implemented the filtering algorithms in constraint Logic programming on Finite Domains (CLP(FD)).
The Capacitated Vehicle Routing Problem with Pick-up, Alternative Delivery and Time Windows (CVRPPADTW) is discussed in the paper. The development of this problem was motivated by postal items distribution issues. In ...
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We present a domain for string decision variables of bounded length, combining features from fixed-length and unbounded-length string solvers to reason on an interval defined by languages of prefixes and suffixes. We ...
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ISBN:
(纸本)9781479929733
We present a domain for string decision variables of bounded length, combining features from fixed-length and unbounded-length string solvers to reason on an interval defined by languages of prefixes and suffixes. We provide a theoretical groundwork for constraint solving on this domain and describe propagation techniques for several common constraints.
This paper introduces a synthesis procedure for the satisfiability problem of RMTL-integral formulas as SAT solving modulo theories. RMTL-integral is a real-time version of metric temporal logic (MTL) extended by a du...
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ISBN:
(纸本)9781728140865
This paper introduces a synthesis procedure for the satisfiability problem of RMTL-integral formulas as SAT solving modulo theories. RMTL-integral is a real-time version of metric temporal logic (MTL) extended by a duration quantifier allowing to measure time durations. For any given formula, a SAT instance modulo the theory of arrays, uninterpreted functions with equality and non-linear real-arithmetic is synthesized and may then be further investigated using appropriate SMT solvers. We show the benefits of using RMTL-integral with the given SMT encoding on a diversified set of examples that include in particular its application in the area of schedulability analysis. Therefore, we introduce a simple language for formalizing schedulability problems and show how to formulate timing constraints as RMTL-integral formulas. Our practical evaluation based on our synthesis and Z3 as back-end SMT solver also shows the feasibility of the overall approach.
Mining high utility itemsets is a keystone in several data analysis tasks. High Utility Itemset Mining generalizes the frequent itemset mining problem by considering item quantities and weights. A high utility itemset...
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ISBN:
(纸本)9783030590659;9783030590642
Mining high utility itemsets is a keystone in several data analysis tasks. High Utility Itemset Mining generalizes the frequent itemset mining problem by considering item quantities and weights. A high utility itemset is a set of items that appears in the transadatabase and having a high importance to the user, measured by a utility function. The utility of a pattern can be quantified in terms of various objective criteria, e.g., profit, frequency, and weight. constraint programming (CP) and Propositional Satisfiability (SAT) based frameworks for modeling and solving pattern mining tasks have gained a considerable attention in recent few years. This paper introduces the first declarative framework for mining high utility itemsets from transaction databases. First, we model the problem of mining high utility itemsets from transaction databases as a propositional satifiability problem. Moreover, to facilitate the mining task, we add an additional constraint to the efficiency of our method by using weighted clique cover problem. Then, we exploit the efficient SAT solving techniques to output all the high utility itemsets in the data that satisfy a user-specified minimum support and minimum utility values. Experimental evaluations on real and synthetic datasets show that the performance of our proposed approach is close to that of the optimal case of state-of-the-art HUIM algorithms.
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