Logical Analysis of Data (LAD) is a powerful technique for data classification based on partially defined Boolean functions. The decision rules for class prediction in LAD are formed out of patterns. According to diff...
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Logical Analysis of Data (LAD) is a powerful technique for data classification based on partially defined Boolean functions. The decision rules for class prediction in LAD are formed out of patterns. According to different preferences in the classification problem, various pattern types have been defined. The generation of these patterns plays a key role in the LAD methodology and represents a computationally hard problem. In this article, we introduce a new approach to pattern generation in LAD based on answer set programming (ASP), which can be applied to all common LAD pattern types.
The rapid advancement of Artificial Intelligence (AI) has catalysed transformative developments across various domains, including tour planning. This paradigm shift has unlocked new opportunities for leveraging AI-dri...
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The rapid advancement of Artificial Intelligence (AI) has catalysed transformative developments across various domains, including tour planning. This paradigm shift has unlocked new opportunities for leveraging AI-driven solutions to optimize tour itineraries, enhance user experiences, and streamline logistical operations. In particular, Artificial General Intelligence (AGI) techniques have been instrumental in revolutionizing tour planning by offering innovative approaches to route optimization, resource allocation, and personalized itinerary generation. This abstract explores the burgeoning landscape of AGI applications in tour planning, highlighting its potential to reshape the tourism industry and improve the efficiency and effectiveness of tour management processes. This research proposes an adaptive tour planning model aimed at addressing the evolving needs and challenges of modern tour operators. Focusing on the Vehicle Routing Problem (VRP), the proposed model integrates novel constraints, including weather and traffic conditions, to enhance its robustness and applicability in real-world scenarios. By incorporating these dynamic factors, the proposed model offers more resilient and responsive routing solutions, capable of adapting to changing environmental conditions and unforeseen disruptions. This innovative approach lays the foundation for the development of more efficient and reliable tour planning systems, paving the way for enhanced user satisfaction and operational efficiency in the tourism sector.
In this article, we propose an extension of answer set programming (ASP) to support declarative reasoning on consumption and production of resources. We call the proposed extension RASP, standing for 'Resourced AS...
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In this article, we propose an extension of answer set programming (ASP) to support declarative reasoning on consumption and production of resources. We call the proposed extension RASP, standing for 'Resourced ASP'. Resources are modeled by introducing special atoms, called amount-atoms, to which we associate quantities that represent the available amount of a certain resource. The 'firing' of a RASP rule involving amount-atoms can both consume and produce resources. A RASP rule can be fired several times, according to its definition and to the available quantities of required resources. We define the semantics for RASP programs by extending the usual answerset semantics. Different answersets correspond to different possible allocations of available resources. We then propose an implementation based on standard ASP-solvers. The implementation consists of a standard translation of each RASP rule into a set of plain ASP-rules and of an inference engine that manages the firing of RASP rules.
answer set programming (ASP) is a knowledge representation and reasoning paradigm with high-level expressive logic-based formalism, and efficient solvers;it is applied to solve hard problems in various domains, such a...
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answer set programming (ASP) is a knowledge representation and reasoning paradigm with high-level expressive logic-based formalism, and efficient solvers;it is applied to solve hard problems in various domains, such as systems biology, wire routing, and space shuttle control. In this paper, we present an application of ASP to housekeeping robotics. We show how the following problems are addressed using computational methods/tools of ASP: (1) embedding commonsense knowledge automatically extracted from the commonsense knowledge base ConceptNet, into high-level representation, and (2) embedding (continuous) geometric reasoning and temporal reasoning about durations of actions, into (discrete) high-level reasoning. We introduce a planning and monitoring algorithm for safe execution of plans, so that robots can recover from plan failures due to collision with movable objects whose presence and location are not known in advance or due to heavy objects that cannot be lifted alone. Some of the recoveries require collaboration of robots. We illustrate the applicability of ASP on several housekeeping robotics problems, and report on the computational efficiency in terms of CPU time and memory.
This survey collects scientific works where answer set programming, a declarative knowledge representation and reasoning formalism, is applied to natural language processing and computational linguistics.
This survey collects scientific works where answer set programming, a declarative knowledge representation and reasoning formalism, is applied to natural language processing and computational linguistics.
The majority of approaches to multicriteria optimization are based on quantitative representations of preferences of a decision maker, in which numerical procedures of multicriteria analysis are used for aggregation p...
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The majority of approaches to multicriteria optimization are based on quantitative representations of preferences of a decision maker, in which numerical procedures of multicriteria analysis are used for aggregation purposes. However, very often qualitative data cannot be known in terms of absolute values so that a qualitative approach is needed. Moreover, the multicriteria methods are directly applicable when alternatives are individuals-then they may be explicitly listed and ordered by an agent. However, sometimes the set of alternatives has combinatorial structure and it must be selected from the set of Cartesian products of value domains of attributes satisfying certain constraints. Then, the space of possible alternatives has a size exponential in the number of variables and ranking all alternatives explicitly is a complex and tedious task. In this paper we propose logic programming with ordered disjunction as a qualitative approach to combinatorial multicriteria decision making, allowing a concise representation of the preference structures, and a human-like form of expressions, being close to natural language, hence providing a good readability and simplicity. A combinatorial multicriteria decision making problem is encoded as a logic program, in which preferences of the decision maker are represented qualitatively. The optimal decision corresponds exactly to the preferred answerset of the program, obtained via the well-known methods of multicriteria analysis.
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.
answer set programming ( ASP) emerged in the late 1990s as a new logic programming paradigm that has been successfully applied in various application domains. Also motivated by the availability of efficient solvers fo...
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answer set programming ( ASP) emerged in the late 1990s as a new logic programming paradigm that has been successfully applied in various application domains. Also motivated by the availability of efficient solvers for propositional satisfiability ( SAT), various reductions from logic programs to SAT were introduced. All these reductions, however, are limited to a subclass of logic programs or introduce new variables or may produce exponentially bigger propositional formulas. In this paper, we present a SAT-based procedure, called ASP-SAT, that ( 1) deals with any (nondisjunctive) logic program, ( 2) works on a propositional formula without additional variables ( except for those possibly introduced by the clause form transformation), and ( 3) is guaranteed to work in polynomial space. From a theoretical perspective, we prove soundness and completeness of ASP-SAT. From a practical perspective, we have ( 1) implemented ASP-SAT in CMODELS, ( 2) extended the basic procedures in order to incorporate the most popular SAT reasoning strategies, and ( 3) conducted an extensive comparative analysis involving other state-of-the-art answerset solvers. The experimental analysis shows that our solver is competitive with the other solvers we considered and that the reasoning strategies that work best on 'small but hard' problems are ineffective on 'big but easy' problems and vice versa.
In this paper we provide an introductory explanation of the underlying semantics of answer set programming in terms of equilibrium logic. Rather than a thorough formal presentation of this formalism and its properties...
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In this paper we provide an introductory explanation of the underlying semantics of answer set programming in terms of equilibrium logic. Rather than a thorough formal presentation of this formalism and its properties, we emphasize the intuitive meaning of its main logical definitions, explaining their effect on some example programs. We also overview some of the main extensions and relations to other logical approaches.
The course timetabling problem can be generally defined as the task of assigning a number of lectures to a limited set of timeslots and rooms, subject to a given set of hard and soft constraints. The modeling language...
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The course timetabling problem can be generally defined as the task of assigning a number of lectures to a limited set of timeslots and rooms, subject to a given set of hard and soft constraints. The modeling language for course timetabling is required to be expressive enough to specify a wide variety of soft constraints and objective functions. Furthermore, the resulting encoding is required to be extensible for capturing new constraints and for switching them between hard and soft, and to be flexible enough to deal with different formulations. In this paper, we propose to make effective use of ASP as a modeling language for course timetabling. We show that our ASP-based approach can naturally satisfy the above requirements, through an ASP encoding of the curriculum-based course timetabling problem proposed in the third track of the second international timetabling competition (ITC-2007). Our encoding is compact and human-readable, since each constraint is individually expressed by either one or two rules. Each hard constraint is expressed by using integrity constraints and aggregates of ASP. Each soft constraint S is expressed by rules in which the head is the form of penalty (S, V, C), and a violation V and its penalty cost C are detected and calculated respectively in the body. We carried out experiments on four different benchmark sets with five different formulations. We succeeded either in improving the bounds or producing the same bounds for many combinations of problem instances and formulations, compared with the previous best known bounds.
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