On the Implementation of Speculative Constraint Processing

Abstract Constraint answer set programming or CASP, for short, is a hybrid approach in automated reasoning putting together the advances of distinct research areas such as answer set programming, constraint processing, and satisfiability modulo theories. CASP demonstrates promising results, including the development of a multitude of solvers: acsolver, clingcon, ezcsp, idp, inca, dingo, mingo, aspmt2smt, clingo[l,dl], and ezsmt. It opens new horizons for declarative programming applications such as solving complex train scheduling problems. Systems designed to find solutions to constraint answer set programs can be grouped according to their construction into, what we call, integrational or translational approaches. The focus of this paper is an overview of the key ingredients of the design of constraint answer set solvers drawing distinctions and parallels between integrational and translational approaches. The paper also provides a glimpse at the kind of programs its users develop by utilizing a CASP encoding of Traveling Salesman problem for illustration. In addition, we place the CASP technology on the map among its automated reasoning peers as well as discuss future possibilities for the development of CASP.

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UAV Path Planning Based on Multi-Stage Constraint Optimization

Drones ◽

10.3390/drones5040144 ◽

2021 ◽

Vol 5 (4) ◽

pp. 144

Author(s):

Yong Shen ◽

Yunlou Zhu ◽

Hongwei Kang ◽

Xingping Sun ◽

Qingyi Chen ◽

...

Keyword(s):

Path Planning ◽

Three Dimensional ◽

Evolutionary Process ◽

Constraint Processing ◽

Multi Stage ◽

Planning Ability ◽

Path Constraints ◽

Aerial Vehicle ◽

Multi Level ◽

Processing Mechanism

Evolutionary Algorithms (EAs) based Unmanned Aerial Vehicle (UAV) path planners have been extensively studied for their effectiveness and high concurrency. However, when there are many obstacles, the path can easily violate constraints during the evolutionary process. Even if a single waypoint causes a few constraint violations, the algorithm will discard these solutions. In this paper, path planning is constructed as a multi-objective optimization problem with constraints in a three-dimensional terrain scenario. To solve this problem in an effective way, this paper proposes an evolutionary algorithm based on multi-level constraint processing (ANSGA-III-PPS) to plan the shortest collision-free flight path of a gliding UAV. The proposed algorithm uses an adaptive constraint processing mechanism to improve different path constraints in a three-dimensional environment and uses an improved adaptive non-dominated sorting genetic algorithm (third edition—ANSGA-III) to enhance the algorithm’s path planning ability in a complex environment. The experimental results show that compared with the other four algorithms, ANSGA-III-PPS achieves the best solution performance. This not only validates the effect of the proposed algorithm, but also enriches and improves the research results of UAV path planning.

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Privacy-Preserving Data Mining

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch044 ◽

2008 ◽

pp. 693-704

Author(s):

Bhavani Thuraisingham

Keyword(s):

Data Mining ◽

Privacy Preserving ◽

Future Research ◽

Inference Problem ◽

Privacy Preserving Data Mining ◽

Privacy Concerns ◽

Security Applications ◽

Constraint Processing ◽

Privacy Constraints ◽

Privacy Problem

This article first describes the privacy concerns that arise due to data mining, especially for national security applications. Then we discuss privacy-preserving data mining. In particular, we view the privacy problem as a form of inference problem and introduce the notion of privacy constraints. We also describe an approach for privacy constraint processing and discuss its relationship to privacy-preserving data mining. Then we give an overview of the developments on privacy-preserving data mining that attempt to maintain privacy and at the same time extract useful information from data mining. Finally, some directions for future research on privacy as related to data mining are given.

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