Petri nets are a class of models of computation used to compactly represent discrete event systems. Among many application domains, they have now become the most prominent formalism to express process models in Proces...
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ISBN:
(纸本)9783031742088;9783031742095
Petri nets are a class of models of computation used to compactly represent discrete event systems. Among many application domains, they have now become the most prominent formalism to express process models in Process Mining, thanks to their formal semantics that enables automated analysis techniques. In this context, model repair is the task of aligning a process model with actual executions of the process. Current solutions to model repair do not allow for embedding domain knowledge, providing guarantees of rigor, and enforcing structural requirements at the same time. In this paper, we fill this gap by proposing an approach based on the Inductive logicprogramming system ILASP. We then implement our approach and perform an experimental evaluation, showing both its expressiveness and feasibility.
Recently forms of abstraction have been proposed for bothlogic programs (LPs) under the answer set semantics (ASP) and for the related formalism of assumption-based argumentation (ABA), e.g., via clustering of atoms ...
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ISBN:
(纸本)9783031742088;9783031742095
Recently forms of abstraction have been proposed for bothlogic programs (LPs) under the answer set semantics (ASP) and for the related formalism of assumption-based argumentation (ABA), e.g., via clustering of atoms or assumptions, in order to simplify a given LP or ABA framework. In both approaches after clustering the original answer sets and assumption sets are over-approximated, withthe aim of avoiding spuriousness. In contrast, in ASP a given LP is syntactically modified to achieve over-approximation, while on ABA the framework is minimally modified and the semantics is abstracted. In this work we follow the latter approach and provide a novel semantical abstraction for LPs and for ABA frameworks corresponding to LPs.
Traditionally, in the argumentation theory literature structured arguments are constructed from rules interpretations aiming to build well-supported deductive evidence. Different from other approaches, we emphasize th...
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ISBN:
(纸本)9783031742088;9783031742095
Traditionally, in the argumentation theory literature structured arguments are constructed from rules interpretations aiming to build well-supported deductive evidence. Different from other approaches, we emphasize the role of investigating general frameworks that can also provide a consistent and well-defined justification for a conclusion that cannot be inferred and there is certainty about it, which we call here NAF-arguments, which have been less explored in the formal argumentation theory, despite its potential use in practical applications for building nuanced well-structured explanations and justifications. this article introduces the so-called semantic argumentation guaranteeing well-known principles for quality in structured argumentation, and withthe ability to generate two types of arguments, those where the conclusion atoms are semantically interpreted as true, and those where the conclusion is assumed to be false, we call them here semantic and NAF-arguments respectively. this framework is defined on the set of all logic programs in terms of rewriting systems based on a confluent set of transformation rules, the so-called Confluent logicprogramming Systems, making this approach a general framework. Additionally, we introduce a method for building such arguments using the program's strata through partial interpretations. We implement our framework named semantic argumentation solver available open source.
My research explores integrating deep learning and logicprogramming to set the basis for a new generation of AI systems. By combining neural networks with Inductive logicprogramming (ILP), the goal is to construct s...
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My research explores integrating deep learning and logicprogramming to set the basis for a new generation of AI systems. By combining neural networks with Inductive logicprogramming (ILP), the goal is to construct systems that make accurate predictions and generate comprehensible rules to validate these predictions. Deep learning models process and analyze complex data, while ILP techniques derive logical rules to prove the network's conclusions. Explainable AI methods, like eXplainable Answer Set programming (XASP), elucidate the reasoning behind these rules and decisions. the focus is on applying ILP frameworks, specifically ILASP and FastLAS, to enhance explainability in various domains. My test cases span weather prediction, the legal field, and image recognition. In weather forecasting, the system will predict events and provides explanations using FastLAS, with plans to integrate recurrent neural networks in the future. In the legal domain, the research focuses on interpreting vague decisions and assisting legal professionals by encoding Italian legal articles and learning reasoning patterns from Court of Cassation decisions using ILASP. For biological laboratories, we will collaborate with a research group to automate spermatozoa morphology classification for Bull Breeding Soundness Evaluation using YOLO networks and ILP to explain classification outcomes. this hybrid approach aims to bridge the gap between the high performance of deep learning models and the transparency of symbolic reasoning, advancing AI by providing interpretable and trustworthy applications.
this special issue of theory and Practice of logicprogramming consists of extended versions of five selected papers from the 3rd international Joint conference on Rules and reasoning (RuleML+RR 2019). RuleML+RR 2019 ...
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this special issue of theory and Practice of logicprogramming consists of extended versions of five selected papers from the 3rd international Joint conference on Rules and reasoning (RuleML+RR 2019). RuleML+RR 2019 was held in conjunction withthe 5th Global conference on artificialintelligence, GCAI 2019, as part of the Bolzano Rules and artificialintelligence Summit in Bolzano, Italy, from 17 to 19 of September 2019.
In the fast-growing area of artificialintelligence (AI), the ability of autonomous agents to engage in complex debates is crucial for consensus building on beliefs, actions, or goals and forms the basis for applicati...
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ISBN:
(纸本)9783031742088;9783031742095
In the fast-growing area of artificialintelligence (AI), the ability of autonomous agents to engage in complex debates is crucial for consensus building on beliefs, actions, or goals and forms the basis for applications in decision-making, planning, opinion polling, and negotiation. In this paper, we leverage the Timed Concurrent Language for Argumentation, a modelling language derived from concurrent programming paradigms and Argumentation theory, to introduce well-known high-level propositions (claim, counter, why, argue, concede, and retract) to model various debate forms, making it a powerful tool for agent interaction. the obtained constructs, specifically designed for multi-agent reasoning and the facilitation of argumentation, define the dialogue language DICLA (DIalogic Concurrent Language for Argumentation) that enables domain experts to employ advanced computational argumentation tools without needing programming skills, bridging the gap between theoretical argumentation models and practical, real-world applications.
Finite variant of Linear Temporal logic (LTLf) is increasingly popular in artificialintelligence (AI).Indeed, several AI applications rely on checking the satisfiability of temporal specifications expressed in LTLf (...
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ISBN:
(纸本)9783031742088;9783031742095
Finite variant of Linear Temporal logic (LTLf) is increasingly popular in artificialintelligence (AI).Indeed, several AI applications rely on checking the satisfiability of temporal specifications expressed in LTLf (e.g., planning, and model-checking). this paper describes ltlf2asp, an ASP-based system for bounded satisfiability checking of LTLf formulae. the approach is based on a natural encoding in ASP of temporal operators that is inspired to SAT-based approaches. Experiments show that our system compares favourably to the state-ofthe-art, and shows the ASP technology stack is a suitable alternative to SAT/SMT solvers for bounded satisfiability checking of LTLf formulae.
this work focuses on quantum programming language and logics for quantum programs. We extend the standard quantum programming language with a parallel operator and an await command. Our extended quantum dynamic logic ...
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ISBN:
(纸本)9789819603534;9789819603541
this work focuses on quantum programming language and logics for quantum programs. We extend the standard quantum programming language with a parallel operator and an await command. Our extended quantum dynamic logic provides a more robust framework for reasoning about the behaviors and properties of quantum programs that utilize these new constructs. these new features enhance the language's capability to handle concurrent quantum operations and synchronization, enabling more advanced and efficient quantum computations.
Common approaches to behavioral artificialintelligence, like behavior trees, utility-based approaches, and machine learning are often lacking with regards to expressivity of the decision-making process. Behavior tree...
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ISBN:
(纸本)9798400706257
Common approaches to behavioral artificialintelligence, like behavior trees, utility-based approaches, and machine learning are often lacking with regards to expressivity of the decision-making process. Behavior trees are too rigid;utility-based approaches are focused on outcomes, not process;and machine learned agents seek only to maximize reward and minimize loss. A combination of AI approaches, however, may provide more nuanced, visible, and coherent processes that can be realized through the performance of character behaviors. In this extended abstract, we present Viv: a system that blends defeasible logicprogramming with dynamic behavior trees. To our knowledge, no extant works have bridged the gap between defeasible reasoning and action, particularly in regards to the performances of virtual nonplayer characters. We begin by explaining the design of our system, which was heavily influenced by the Stanislavski method of acting. We then describe the technical implementation of our system. Finally, we describe future directions of research that could provide benefit to, or benefit from, our system.
the analysis of properties of consequence operators has been a very active field in the formative years of non-monotonic reasoning. One possible approach to do this is to start with a model-theoretic semantics and the...
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ISBN:
(纸本)9783031742088;9783031742095
the analysis of properties of consequence operators has been a very active field in the formative years of non-monotonic reasoning. One possible approach to do this is to start with a model-theoretic semantics and then to study the logical consequence relation induced by that semantics. In this paper we follow that approach and analyse resulting consequence operators of so-called characterization logics. Roughly speaking, a characterization logic characterizes, via its own notion of ordinary equivalence, another logic's notion of strong equivalence. For example, the logic of here and there is a characterization logic for answer set programs, because strong equivalence of the latter is characterized by ordinary equivalence of the former. In previous work, we showed that the consideration of finite knowledge bases only - a common assumption in the field of knowledge representation - guarantees the existence (and uniqueness) of characterization logics. In this paper, we apply this existence result to the field of abstract argumentation. We show that the associated consequence operator outputs a so-called reverse kernel, a useful construct that received comparably little attention in the literature so far. As an aside, we clarify that for several well-known logics, their canonical characterization consequence operators are well-behaved.
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