This paper demonstrates the use of logical English as a logic programming language that can be interpreted by the s(CASP) reasoner. It shows how legal knowledge and unknown information can be expressed in a form of En...
详细信息
From its beginning in the 1950s, noncomputing academics were skeptical about computer science because it seemed strong on technology and weak on theory. To answer the critics and shore up their case, computer scientis...
详细信息
From its beginning in the 1950s, noncomputing academics were skeptical about computer science because it seemed strong on technology and weak on theory. To answer the critics and shore up their case, computer scientists turned to a rich trove of computationalmethods from logic *** from ancient times focused on methods of manipulating symbols that could be performed by people untrained in mathematics. Examples include ancient Babylonian algorithm- like step-by-step rules, Greek mathematical procedures like the Euclidean algorithm or the sieve of Eratosthenes, and al-Khwarizmi’s algorithmic techniques. In the twentieth century, the mathematical logicians Turing, G€odel, Church, Kleene, and Post provided a solid foundational theory for the new field of computer science, showingwhat can and cannot becomputed.
Declarative process discovery algorithms aim to identify a subset of relationships between process’ activities ("constraints") that implicitly define the acceptable behavior of a process given a bag of its ...
详细信息
In this paper, we present a goal-directed proof procedure for ASP with abduction. Our proposed procedure in this paper is correct for any consistent abductive framework proposed in [Kakas90a]. In other words, if the p...
详细信息
This article explores the intertwining connections among artificial intelligence, machine learning, digital transformation, and computational sustainability, detailing how these elements jointly empower citizens withi...
详细信息
This article explores the intertwining connections among artificial intelligence, machine learning, digital transformation, and computational sustainability, detailing how these elements jointly empower citizens within a smart city framework. As technological advancement accelerates, smart cities harness these innovations to improve residents' quality of life. Artificial intelligence and machine learning act as data analysis powerhouses, making urban living more personalized, efficient, and automated, and are pivotal in managing complex urban infrastructures, anticipating societal requirements, and averting potential crises. Digital transformation transforms city operations by weaving digital technology into every facet of urban life, enhancing value delivery to citizens. Computational sustainability, a fundamental goal for smart cities, harnesses artificial intelligence, machine learning, and digital resources to forge more environmentally responsible cities, minimize ecological impact, and nurture sustainable development. The synergy of these technologies empowers residents to make well-informed choices, actively engage in their communities, and adopt sustainable lifestyles. This discussion illuminates the mechanisms and implications of these interconnections for future urban existence, ultimately focusing on empowering citizens in smart cities.
Cognitive theories for reasoning are about understanding how humans come to conclusions from a set of premises. Starting from hypothetical thoughts, we are interested which are the implications behind basic everyday l...
详细信息
This paper is an extended abstract of: J. Arias, M. Moreno-Rebato, J. A. Rodriguez-García, S. Ossowski, Modeling Administrative Discretion Using Goal-Directed Answer Set programming, in: Advances in Artificial In...
详细信息
Search is one of the more common strategies used by problem-solving agents. For many hard problems, a backtracking search can be the most effective approach for finding a solution. logic programming languages provide,...
详细信息
DLV2 is an AI tool for Knowledge Representation and Reasoning which supports Answer Set programming (ASP)-a logic-based declarative formalism, successfully used in both academic and industrial applications. Given a lo...
详细信息
The Boolean satisfiability problem, a renowned NP -complete challenge in computer science, has recently garnered interest in the Discrete Hopfield Neural Network - Satisfiability model. This model adeptly integrates l...
详细信息
The Boolean satisfiability problem, a renowned NP -complete challenge in computer science, has recently garnered interest in the Discrete Hopfield Neural Network - Satisfiability model. This model adeptly integrates logical rules into Hopfield networks, excelling in locating global minima for traditional SAT problems. However, it faces efficiency challenges when dealing with SAT problems characterized by dynamic evolution constraints due to its static network architecture. During dynamic evolution iterations, there is a significant exponential increase in the computational costs due to redundant and repetitive computations. In order to address this challenge, this paper introduces a dynamic evolution variant of the Discrete Hopfield Neural Network - Satisfiability model. In extensive simulation experiments, we progressively augmented the number of constraint clauses from 1 to 1500, seeking global minima for CNF problems. The proposed model exhibited congruent performance with the traditional model, achieving a Global Minimum Ratio of 1 and a Hamming distance of 0. Crucially, the proposed model minimized CPU utilization and neared zero error metrics, while the traditional model experienced exponential CPU and error metric escalation. These outcomes affirm the proposed model's robust global search capabilities and high precision, aligning with the traditional model. Furthermore, owing to this model's incorporation of not only temporal constraint increment operator but also innovative real-time learning techniques and clever integration methods, along with the establishment of a novel real-time decision mechanism, the proposed model effectively addresses the issues of redundancy and repeated calculations inherent in traditional models. This results in a stable and significantly improved computational speed. Additionally, this model's dynamic evolution network architecture is specifically designed to accommodate arbitrary and efficient extensions of dynamic constraints,
暂无评论