The problem of finding a Master Surgical Schedule (MSS) consists of scheduling different specialties to the operating rooms of a hospital clinic. To produce a proper MSS, each specialty must be assigned to some operat...
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The problem of finding a Master Surgical Schedule (MSS) consists of scheduling different specialties to the operating rooms of a hospital clinic. To produce a proper MSS, each specialty must be assigned to some operating room. The number of assignments is different for each specialty and can vary during the considered planning horizon. Realizing a satisfying schedule is of upmost importance for a hospital clinic: recently, a compact solution based on the logic-based methodology of answer set programming (ASP) to the MSS problem has been introduced and tested on synthetic data, with satisfying results. However, even more important is to be able to (i) reschedule efficiently in case a computed schedule cannot be fully implemented due to unavailability, and (ii) test the obtained solution on real data. In this paper, we design and implement a rescheduling solution based on ASP, and test both our scheduling and rescheduling solutions on real data from ASL1 Liguria in Italy. The experiments show that our ASP solutions provide satisfying results, also when tested on real data.
Payroll management is a critical business task that is subject to a large number of rules, which vary widely between companies, sectors, and countries. Moreover, the rules are often complex and change regularly. There...
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Payroll management is a critical business task that is subject to a large number of rules, which vary widely between companies, sectors, and countries. Moreover, the rules are often complex and change regularly. Therefore, payroll management systems must be flexible in design. In this paper, we suggest an approach based on a flexible answer set programming (ASP) model and an easy-to-read tabular representation based on the decision model and notation standard. It allows HR consultants to represent complex rules without the need for a software engineer and to ultimately design payroll systems for a variety of different scenarios. We show how the multi-shot solving capabilities of the clingo ASP system can be used to reach the performance that is necessary to handle real-world instances.
In automotive industry, validation and maintenance of product configuration data is a complex task. Both orders from the customers and new product line designs from the R&D department are subject to a set of confi...
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In automotive industry, validation and maintenance of product configuration data is a complex task. Both orders from the customers and new product line designs from the R&D department are subject to a set of configuration rules to be satisfied. In this work, non-monotonic computational logic, answer set programming in particular, is applied to industrial-scale automotive product configuration problems. This methodology provides basic validation of the product configuration documentation and validation of single product orders, where Reiter style diagnosis provides minimal changes needed to correct an invalid order or a product configuration rule set. In addition, a method for discovering groups of product configuration variables that are strongly related can be obtained by small modification of the basic logic program, and by the usage of cautious and brave reasoning methods. As a result, options that are used in every, or respectively in no configuration, can easily be identified, as well as groups of options that are always used together or not at all. Finally it is possible to single out mandatory and obsolete options, relative to a preselected set of included or excluded options. Experimental results on an industrial dataset show applicability, example results, and computational feasibility with computation times on the order of seconds using a state-of-the-art answerset solver on standard PC hardware.
For planning an assembly of a product from a given set of parts, robots necessitate certain cognitive skills: high-level planning is needed to decide the order of actuation actions, while geometric reasoning is needed...
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For planning an assembly of a product from a given set of parts, robots necessitate certain cognitive skills: high-level planning is needed to decide the order of actuation actions, while geometric reasoning is needed to check the feasibility of these actions. For collaborative assembly tasks with humans, robots require further cognitive capabilities, such as commonsense reasoning, sensing, and communication skills, not only to cope with the uncertainty caused by incomplete knowledge about the humans' behaviors but also to ensure safer collaborations. We propose a novel method for collaborative assembly planning under uncertainty, that utilizes hybrid conditional planning extended with commonsense reasoning and a rich set of communication actions for collaborative tasks. Our method is based on answer set programming. We show the applicability of our approach in a real-world assembly domain, where a bi-manual Baxter robot collaborates with a human teammate to assemble furniture.
We present an answer set programming realization of the h-approximation (HPX) theory [8] as an efficient and provably sound reasoning method for epistemic planning and projection problems that involve postdictive reas...
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We present an answer set programming realization of the h-approximation (HPX) theory [8] as an efficient and provably sound reasoning method for epistemic planning and projection problems that involve postdictive reasoning. The efficiency of HPX stems from an approximate knowledge state representation that involves only a linear number of state variables, as compared to an exponential number for theories that utilize a possible-worlds based semantics. This causes a relatively low computational complexity, i.e, the planning problem is in NP under reasonable restrictions, at the cost that HPX is incomplete. In this paper, we use the implementation of HPX to investigate the incompleteness issue and present an empirical evaluation of the solvable fragment and its performance. We find that the solvable fragment of HPX is indeed reasonable and fairly large: in average about 85% of the considered projection problem instances can be solved, compared to a PWS-based approach with exponential complexity as baseline. In addition to the empirical results, we demonstrate the manner in which HPX can be applied in a real robotic control task within a smart home, where our scenario illustrates the usefulness of postdictive reasoning to achieve error-tolerance by abnormality detection in a high-level decision-making task. (C) 2015 Elsevier B.V. All rights reserved.
answer set programming is a declarative programming paradigm rooted in logic programming and non-monotonic reasoning. This formalism has become a host for expressing knowledge representation problems, which reinforces...
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answer set programming is a declarative programming paradigm rooted in logic programming and non-monotonic reasoning. This formalism has become a host for expressing knowledge representation problems, which reinforces the interest in efficient methods for computing answersets of a logic program. The complexity of various reasoning tasks for general answer set programming has been amply studied and is understood quite well. In this paper, we present a language fragment in which the arities of predicates are bounded by a constant. Subsequently, we analyze the complexity of various reasoning tasks and computational problems for this fragment, comprising answerset existence, brave and cautious reasoning, and strong equivalence. Generally speaking, it turns out that the complexity drops significantly with respect to the full non-ground language, but is still harder than for the respective ground or propositional languages. These results have several implications, most importantly for solver implementations: Virtually all currently available solvers have exponential (in the size of the input) space requirements even for programs with bounded predicate arities, while our results indicate that for those programs polynomial space should be sufficient. This can be seen as a manifestation of the "grounding bottleneck" (meaning that programs are first instantiated and then solved) from which answer set programming solvers currently suffer. As a final contribution, we provide a sketch of a method that can avoid the exponential space requirement for programs with bounded predicate arities.
Distributed authorization is an essential issue in computer security. Recent research shows that trust management is a promising approach for the authorization in distributed environments. There are two key issues for...
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Distributed authorization is an essential issue in computer security. Recent research shows that trust management is a promising approach for the authorization in distributed environments. There are two key issues for a trust management system: how to design an expressive high-level policy language and how to solve the compliance-checking problem (Blaze et al. in Proceedings of the Symposium on Security and Privacy, pp. 164-173, 1996;Proceedings of 2nd International Conference on Financial Cryptography (FC'98). LNCS, vol.1465,pp.254-274, 1998), where ordinary logic programming has been used to formalize various distributed authorization policies (Li et al. in Proceedings of the 2002 IEEE Symposium on Security and Privacy, pp. 114-130, 2002;ACM Trans. Inf. Syst. Secur. (TISSEC) 6(1): 128-171, 2003). In this paper, we employ answer set programming to deal with many complex issues associated with the distributed authorization along the trust management approach. In particular, we propose a formal authorization language AL providing its semantics through answer set programming. Using language AL, we cannot only express nonmonotonic delegation policies which have not been considered in previous approaches, but also represent the delegation with depth, separation of duty, and positive and negative authorizations. We also investigate basic computational properties related to our approach. Through two case studies. we further illustrate the application of our approach in distributed environments.
This paper discusses an extension of answer set programming (ASP) called Hybrid answer set programming (H-ASP) which allows the user to reason about dynamical systems that exhibit both discrete and continuous aspects....
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This paper discusses an extension of answer set programming (ASP) called Hybrid answer set programming (H-ASP) which allows the user to reason about dynamical systems that exhibit both discrete and continuous aspects. The unique feature of Hybrid ASP is that it allows the use of ASP type rules as controls for when to apply algorithms to advance the system to the next position. That is, if the prerequisites of a rule are satisfied and the constraints of the rule are not violated, then the algorithm associated with the rule is invoked. (C) 2013 Elsevier B.V. All rights reserved.
Aggregates are among the most frequently used linguistic extensions of answer set programming. The result of an aggregation may introduce new constants during the instantiation of the input program, a feature known as...
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Aggregates are among the most frequently used linguistic extensions of answer set programming. The result of an aggregation may introduce new constants during the instantiation of the input program, a feature known as value invention. When the aggregation involves literals whose truth value is undefined at instantiation time, modern grounders introduce several instances of the aggregate, one for each possible interpretation of the undefined literals. This paper introduces new data structures and techniques to handle such cases, and more in general aggregations on the same aggregate set identified in the ground program in input. The proposed solution reduces the memory footprint of the solver without sacrificing efficiency. On the contrary, the performance of the solver may improve thanks to the addition of some simple entailed clauses which are not easily discovered otherwise, and since redundant computation is avoided during propagation. Empirical evidence of the potential impact of the proposed solution is given.
Aggregates are among the most important linguistic extensions of answer set programming (ASP), allowing for compact representations of properties and inductive definitions involving sets of propositions. Common use ca...
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Aggregates are among the most important linguistic extensions of answer set programming (ASP), allowing for compact representations of properties and inductive definitions involving sets of propositions. Common use cases of aggregates in ASP are reported in this paper, which mainly focus on the semantics implemented by mainstream solvers, namely the F-stable model semantics. Other well-established semantics are also briefly discussed, providing a historical perspective on the foundation of logic programs with aggregates.
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