Stream reasoning systems are designed for complex decision-making from possibly infinite, dynamic streams of data. Modern approaches to stream reasoning are usually performing their computations using stand-alone solv...
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Stream reasoning systems are designed for complex decision-making from possibly infinite, dynamic streams of data. Modern approaches to stream reasoning are usually performing their computations using stand-alone solvers, which incrementally update their internal state and return results as the new portions of data streams are pushed. However, the performance of such approaches degrades quickly as the rates of the input data and the complexity of decision problems are growing. This problem was already recognized in the area of stream processing, where systems became distributed in order to allocate vast computing resources provided by clouds. In this paper we propose a distributed approach to stream reasoning that can efficiently split computations among different solvers communicating their results over data streams. Moreover, in order to increase the throughput of the distributed system, we suggest an interval-based semantics for the LARS language, which enables significant reductions of network traffic. Performed evaluations indicate that the distributed stream reasoning significantly outperforms existing stand-alone LARS solvers when the complexity of decision problems and the rate of incoming data are increasing.
logic-based approaches to AI have the advantage that their behaviour can in principle be explained by providing their users with proofs for the derived consequences. However, if such proofs get very large, then it may...
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The proceedings contain 32 papers. The special focus in this conference is on Foundations of Software Science and Computational Structures. The topics include: Neural Flocking: MPC-Based Supervised Learning of Flockin...
ISBN:
(纸本)9783030452308
The proceedings contain 32 papers. The special focus in this conference is on Foundations of Software Science and Computational Structures. The topics include: Neural Flocking: MPC-Based Supervised Learning of Flocking Controllers;general Supervised Learning as Change Propagation with Delta Lenses;non-idempotent Intersection Types in logical Form;on Computability of Data Word Functions Defined by Transducers;minimal Coverability Tree Construction Made Complete and Efficient;constructing Infinitary Quotient-Inductive Types;relative Full Completeness for Bicategorical Cartesian Closed Structure;a Duality Theoretic View on Limits of Finite Structures;correctness of Automatic Differentiation via Diffeologies and Categorical Gluing;deep Induction: Induction Rules for (Truly) Nested Types;exponential Automatic Amortized Resource Analysis;on Well-Founded and Recursive Coalgebras;concurrent Kleene Algebra with Observations: From Hypotheses to Completeness;graded Algebraic Theories;a Curry-style Semantics of Interaction: From Untyped to Second-Order Lazy λμ-Calculus;an Axiomatic Approach to Reversible Computation;an Auxiliary logic on Trees: on the Tower-Hardness of logics Featuring Reachability and Submodel Reasoning;the Inconsistent Labelling Problem of Stutter-Preserving Partial-Order Reduction;semantical Analysis of Contextual Types;ambiguity, Weakness, and Regularity in Probabilistic Büchi Automata;Local Local Reasoning: A BI-Hyperdoctrine for Full Ground Store;quantum programming with Inductive Datatypes: Causality and Affine Type theory;timed Negotiations;spinal Atomic Lambda-Calculus;learning Weighted Automata over Principal Ideal Domains;the Polynomial Complexity of Vector Addition Systems with States;cartesian Difference Categories;contextual Equivalence for Signal Flow Graphs;parameterized Synthesis for Fragments of First-Order logic Over Data Words;controlling a Random Population;decomposing Probabilistic Lambda-Calculi.
Assumption-based argumentation is one of the most prominent formalisms for logical (or structured) argumentation. It has been shown useful for representing defeasible reasoning and has tight links to logicprogramming...
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ISBN:
(纸本)9783030205287;9783030205270
Assumption-based argumentation is one of the most prominent formalisms for logical (or structured) argumentation. It has been shown useful for representing defeasible reasoning and has tight links to logicprogramming In this paper we study the Dung semantics for extended forms of assumption-based argumentation frameworks (ABFs), based on any contrapositive propositional logic, and whose defeasible rules are expressed by arbitrary formulas in that logic. In particular, new results on the well-founded semantics for such ABFs are reported, the redundancy of the closure condition is shown, and the use of disjunctive attacks is investigated. Finally, some useful properties of the generalized frameworks are considered.
We present Web-STAR, an online platform for story understanding built on top of the STAR reasoning engine for STory comprehension through ARgumentation. The platform includes a web-based integrated development environ...
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We present Web-STAR, an online platform for story understanding built on top of the STAR reasoning engine for STory comprehension through ARgumentation. The platform includes a web-based integrated development environment, integration with the STAR system, and a web service infrastructure to support integration with other systems that rely on story understanding functionality to complete their tasks. The platform also delivers a number of "social" features, including a community repository for public story sharing with a built-in commenting system, and tools for collaborative story editing that can be used for team development projects and for educational purposes.
Although parity constraints are at the heart of many relevant reasoning modes like sampling or model counting, little attention has so far been paid to their integration into ASP systems. We address this shortcoming a...
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ISBN:
(纸本)9783030205287;9783030205270
Although parity constraints are at the heart of many relevant reasoning modes like sampling or model counting, little attention has so far been paid to their integration into ASP systems. We address this shortcoming and investigate a variety of alternative approaches to implementing parity constraints, ranging from rather basic ASP encodings to more sophisticated theory propagators (featuring Gauss-Jordan elimination). All of them are implemented in the xorro system by building on the theory reasoning capabilities of the ASP system dingo. Our comparative empirical study investigates the impact of the number and size of parity constraints on performance and indicates the merits of the respective implementation techniques. Finally, we benefit from parity constraints to equip xorro with means to sample answer sets, paving the way for new applications of ASP.
Artificial intelligence (AI) approaches to problem-solving and decision-making are becoming more and more complex, leading to a decrease in the understandability of solutions. The European Union's new General Data...
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Artificial intelligence (AI) approaches to problem-solving and decision-making are becoming more and more complex, leading to a decrease in the understandability of solutions. The European Union's new General Data Protection Regulation tries to tackle this problem by stipulating a "right to explanation" for decisions made by AI systems. One of the AI paradigms that may be affected by this new regulation is answer set programming (ASP). Thanks to the emergence of efficient solvers, ASP has recently been used for problem-solving in a variety of domains, including medicine, cryptography, and biology. To ensure the successful application of ASP as a problem-solving paradigm in the future, explanations of ASP solutions are crucial. In this survey, we give an overview of approaches that provide an answer to the question of why an answer set is a solution to a given problem, notably off-line justifications, causal graphs, argumentative explanations, and why-not provenance, and highlight their similarities and differences. Moreover, we review methods explaining why a set of literals is not an answer set or why no solution exists at all.
Sequent-type proof systems constitute an important and widely-used class of calculi well-suited for analysing proof search. In this paper, we introduce a sequent-type calculus for a variant of default logic employing ...
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ISBN:
(纸本)9783030205287;9783030205270
Sequent-type proof systems constitute an important and widely-used class of calculi well-suited for analysing proof search. In this paper, we introduce a sequent-type calculus for a variant of default logic employing Lukasiewicz's three-valued logic as the underlying base logic. This version of default logic has been introduced by Radzikowska addressing some representational shortcomings of standard default logic. More specifically, our calculus axiomatises brave reasoning for this version of default logic, following the sequent method first introduced in the context of nonmonotonic reasoning by Bonatti, which employs a complementary calculus for axiomatising invalid formulas, taking care of expressing the consistency condition of defaults.
Satisfiability Modulo Theories (SMT) is a well-established methodology that generalises propositional satisfiability (SAT) by adding support for a variety of theories such as integer arithmetic and bit-vector operatio...
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