We propose an interpretation of the first-order answer set programming (FOASP) in terms of intuitionistic proof theory. It is obtained by two polynomial translations between FOASP and the bounded-arity fragment of the...
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We propose an interpretation of the first-order answer set programming (FOASP) in terms of intuitionistic proof theory. It is obtained by two polynomial translations between FOASP and the bounded-arity fragment of the Sigma(1) level of the Mints hierarchy in first-order intuitionistic logic. It follows that Sigma(1) formulas using predicates of fixed arity (in particular unary) is of the same strength as FOASP. Our construction reveals a close similarity between constructive provability and stable entailment, or equivalently, between the construction of an answerset and an intuitionistic refutation. This paper is under consideration for publication in Theory and Practice of Logic programming.
Meta-Interpretive Learning (MIL) learns logic programs from examples by instantiating meta-rules, which is implemented by the Metagol system based on Prolog. Viewing MIL-problems as combinatorial search problems, they...
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Meta-Interpretive Learning (MIL) learns logic programs from examples by instantiating meta-rules, which is implemented by the Metagol system based on Prolog. Viewing MIL-problems as combinatorial search problems, they can alternatively be solved by employing answer set programming (ASP), which may result in performance gains as a result of efficient conflict propagation. However, a straightforward ASP-encoding of MIL results in a huge search space due to a lack of procedural bias and the need for grounding. To address these challenging issues, we encode MIL in the HEX-formalism, which is an extension of ASP that allows us to outsource the background knowledge, and we restrict the search space to compensate for a procedural bias in ASP. This way, the import of constants from the background knowledge can for a given type of meta-rules be limited to relevant ones. Moreover, by abstracting from term manipulations in the encoding and by exploiting the HEX interface mechanism, the import of such constants can be entirely avoided in order to mitigate the grounding bottleneck. An experimental evaluation shows promising results.
Automated storage and retrieval systems are principal components of modern production and warehouse facilities. In particular, automated guided vehicles nowadays substitute human-operated pallet trucks in transporting...
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Automated storage and retrieval systems are principal components of modern production and warehouse facilities. In particular, automated guided vehicles nowadays substitute human-operated pallet trucks in transporting production materials between storage locations and assembly stations. While low-level control systems take care of navigating such driverless vehicles along programmed routes and avoid collisions even under unforeseen circumstances, in the common case of multiple vehicles sharing the same operation area, the problem remains how to set up routes such that a collection of transport tasks is accomplished most effectively. We address this prevalent problem in the context of car assembly at Mercedes-Benz Ludwigsfelde GmbH, a large-scale producer of commercial vehicles, where routes for automated guided vehicles used in the production process have traditionally been hand-coded by human engineers. Such adhoc methods may suffice as long as a running production process remains in place, while any change in the factory layout or production targets necessitates tedious manual reconfiguration, not to mention the missing portability between different production plants. Unlike this, we propose a declarative approach based on answer set programming to optimize the routes taken by automated guided vehicles for accomplishing transport tasks. The advantages include a transparent and executable problem formalization, provable optimality of routes relative to objective criteria, as well as elaboration tolerance towards particular factory layouts and production targets. Moreover, we demonstrate that our approach is efficient enough to deal with the transport tasks evolving in realistic production processes at the car factory of Mercedes-Benz Ludwigsfelde GmbH.
answer set programming (ASP) is a declarative language for nonmonotonic reasoning based on stable model semantics. Astable model is a classical model of the input program satisfying the following stability condition: ...
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answer set programming (ASP) is a declarative language for nonmonotonic reasoning based on stable model semantics. Astable model is a classical model of the input program satisfying the following stability condition: only necessary information is included in the model under the assumptions provided by the model itself for the unknown knowledge in the program, where unknown knowledge is encoded by means of default negation. Rational agents acting in the real world often have to reason in presence of unknown knowledge. It is also common that real world agents cannot meet all their desiderata. For this reason, ASP programs may include weak constraints for representing numerical preferences over jointly incompatible conditions. Stable models are therefore associated with a cost given by the unsatisfied weak constraints, and stable models of minimum cost are preferred. This paper summarizes algorithms and strategies for computing optimal stable models, and hints on how these algorithms can be extended to handle some qualitative preferences that have a natural representation in circumscription, that is, those based on subset-minimality and superset-maximality.
We propose an interpretation of the first-order answer set programming (FOASP) in terms of intuitionistic proof theory. It is obtained by two polynomial translations between FOASP and the bounded-arity fragment of the...
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We propose an interpretation of the first-order answer set programming (FOASP) in terms of intuitionistic proof theory. It is obtained by two polynomial translations between FOASP and the bounded-arity fragment of the Sigma(1) level of the Mints hierarchy in first-order intuitionistic logic. It follows that Sigma(1) formulas using predicates of fixed arity (in particular unary) is of the same strength as FOASP. Our construction reveals a close similarity between constructive provability and stable entailment, or equivalently, between the construction of an answerset and an intuitionistic refutation. This paper is under consideration for publication in Theory and Practice of Logic programming.
Non-stationary domains, where unforeseen changes happen, present a challenge for agents to find an optimal policy for a sequential decision making problem. This work investigates a solution to this problem that combin...
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Non-stationary domains, where unforeseen changes happen, present a challenge for agents to find an optimal policy for a sequential decision making problem. This work investigates a solution to this problem that combines Markov Decision Processes (MDP) and Reinforcement Learning (RL) with answer set programming (ASP) in a method we call ASP(RL). In this method, answer set programming is used to find the possible trajectories of an MDP, from where Reinforcement Learning is applied to learn the optimal policy of the problem. Results show that ASP(RL) is capable of efficiently finding the optimal solution of an MDP representing non-stationary domains.
Spatial puzzles are interesting domains to investigate problem solving, since the reasoning processes involved in reasoning about spatial knowledge is one of the essential items for an agent to interact in the human e...
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ISBN:
(纸本)9781538680230
Spatial puzzles are interesting domains to investigate problem solving, since the reasoning processes involved in reasoning about spatial knowledge is one of the essential items for an agent to interact in the human environment. With this in mind, the goal of this work is to investigate the knowledge representation and reasoning process related to the solution of a spatial puzzle, the Fisherman's Folly, composed of flexible string, rigid objects and holes. To achieve this goal, the present paper uses heuristics (obtained after solving a relaxed version of the puzzle) to accelerate the learning process, while applying a method that combines answer set programming (ASP) with Reinforcement learning (RL), the oASP(MDP) algorithm, to find a solution to the puzzle. ASP is the logic language chosen to build the set of states and actions of a Markov Decision Process (MDP) representing the domain, where RL is used to learn the optimal policy of the problem.
answer set programming is a declarative problem solving approach, initially tailored to modelling problems in the area of Knowledge Representation and Reasoning. In this article, we provide a knowledge-based system, c...
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ISBN:
(纸本)9781538674499
answer set programming is a declarative problem solving approach, initially tailored to modelling problems in the area of Knowledge Representation and Reasoning. In this article, we provide a knowledge-based system, capable of representing and reasoning about legal knowledge in the context of answer set programming thus, modelling non-monotonicity that is inherent in legal arguments. The work, although limited to a specific indicative domain, namely, university regulations, has a variety of extensions. The overall approach constitutes a representative implementation of the answer set programming's modelling methodology, as well as an enhancing of the bond between Artificial Intelligence and Legal Science, bringing us a step closer to a successful development of an automated legal reasoning system for real-world applications.
The paper presents some applications in planning and multi-agent systems of answer set programming. It highlights the benefits of answer set programming based techniques in these applications. It also describes a clas...
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ISBN:
(纸本)9783319616605;9783319616599
The paper presents some applications in planning and multi-agent systems of answer set programming. It highlights the benefits of answer set programming based techniques in these applications. It also describes a class of multi-agent planning problems that is challenging to answer set programming.
Cell classifier circuits are synthetic biological circuits capable of distinguishing between different cell states depending on specific cellular markers and engendering a state-specific response. An example are class...
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Cell classifier circuits are synthetic biological circuits capable of distinguishing between different cell states depending on specific cellular markers and engendering a state-specific response. An example are classifiers for cancer cells that recognize whether a cell is healthy or diseased based on its miRNA fingerprint and trigger cell apoptosis in the latter case. Binarization of continuous miRNA expression levels allows to formalize a classifier as a Boolean function whose output codes for the cell condition. In this framework, the classifier design problem consists of finding a Boolean function capable of reproducing correct labelings of miRNA profiles. The specifications of such a function can then be used as a blueprint for constructing a corresponding circuit in the lab. To find an optimal classifier both in terms of performance and reliability, however, accuracy, design simplicity and constraints derived from availability of molcular building blocks for the classifiers all need to be taken into account. These complexities translate to computational difficulties, so currently available methods explore only part of the design space and consequently are only capable of calculating locally optimal designs. We present a computational approach for finding globally optimal classifier circuits based on binarized miRNA datasets using answer set programming for efficient scanning of the entire search space. Additionally, the method is capable of computing all optimal solutions, allowing for comparison between optimal classifier designs and identification of key features. Several case studies illustrate the applicability of the approach and highlight the quality of results in comparison with a state of the art method. The method is fully implemented and a comprehensive performance analysis demonstrates its reliability and scalability.
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