Cardinality estimation (CardEst) is a critical aspect of query optimization. Traditionally, it leverages statistics built directly over the data. However, organizational policies (e.g., regulatory compliance) may restrict global data access. Fortunately, query-driven cardinality estimation can learn CardEst models using query workloads. However, existing query-driven models often require access to data or summaries for best performance, and they assume perfect training workloads with complete and balanced join templates (or join graphs). Such assumptions rarely hold in real-world scenarios, in which join templates are incomplete and imbalanced. We present GRASP, a data-agnostic cardinality learning system designed to work under these real-world constraints. GRASP's compositional design generalizes to unseen join templates and is robust to join template imbalance. It also introduces a new per-table CardEst model that handles value distribution shifts for range predicates, and a novel learned count sketch model that captures join correlations across base relations. Across three database instances, we demonstrate that GRASP consistently outperforms existing query-driven models on imperfect workloads, both in terms of estimation accuracy and query latency. Remarkably, GRASP achieves performance comparable to, or even surpassing, traditional approaches built over the underlying data on the complex CEB-IMDb-full benchmark -- despite operating without any data access and using only 10% of all possible join templates.

In this paper, we present a general framework for ranking sets of arguments in abstract argumentation based on their plausibility of acceptance. We present a generalisation of Dung's extension semantics as extension-ranking semantics, which induce a preorder over the power set of all arguments, allow ing us to state that one set is "closer" to being acceptable than another . To evaluate the extension-ranking semantics, we introduce a number of p rinciples that a well-behaved extension-ranking semantics should satisfy. W e consider several simple base relations, each of which models a single central a spect of argumentative reasoning. The combination of these base relations provides us with a family of extension-ranking semantics. We also adapt a numb er of approaches from the literature for ranking extensions to be us able in the context of extension-ranking semantics, and evaluate their beha viour. Keywords: Abstract Argumentation, Ranking Sets of Objects, Extension-ranking semantics 1. Introduction Formal argumentation [7] is concerned with models of rational decis ion-making based on representations of arguments and their relations.

Anti-unification (AU) is a fundamental operation for generalization computation used for inductive inference. It is the dual operation to unification, an operation at the foundation of automated theorem proving. Interest in AU from the AI and related communities is growing, but without a systematic study of the concept nor surveys of existing work, investigations often resort to developing application-specific methods that existing approaches may cover. We provide the first survey of AU research and its applications and a general framework for categorizing existing and future developments.

Machine learning is rapidly being used in database research to improve the effectiveness of numerous tasks included but not limited to query optimization, workload scheduling, physical design, etc. Currently, the research focus has been on replacing a single database component responsible for one task by its learning-based counterpart. However, query performance is not simply determined by the performance of a single component, but by the cooperation of multiple ones. As such, learning based database components need to collaborate during both training and execution in order to develop policies that meet end performance goals. Thus, the paper attempts to address the question "Is it possible to design a database consisting of various learned components that cooperatively work to improve end-to-end query latency?". To answer this question, we introduce MADB (Multi-Agent DB), a proof-of-concept system that incorporates a learned query scheduler and a learned query optimizer. MADB leverages a cooperative multi-agent reinforcement learning approach that allows the two components to exchange the context of their decisions with each other and collaboratively work towards reducing the query latency. Preliminary results demonstrate that MADB can outperform the non-cooperative integration of learned components.

Allen's Interval Algebra constitutes a framework for reasoning about temporal information in a qualitative manner. In particular, it uses intervals, i.e., pairs of endpoints, on the timeline to represent entities corresponding to actions, events, or tasks, and binary relations such as precedes and overlaps to encode the possible configurations between those entities. Allen's calculus has found its way in many academic and industrial applications that involve, most commonly, planning and scheduling, temporal databases, and healthcare. In this paper, we present a novel encoding of Interval Algebra using answer-set programming (ASP) extended by difference constraints, i.e., the fragment abbreviated as ASP(DL), and demonstrate its performance via a preliminary experimental evaluation. Although our ASP encoding is presented in the case of Allen's calculus for the sake of clarity, we suggest that analogous encodings can be devised for other point-based calculi, too.

Spatial information is often expressed using qualitative terms such as natural language expressions instead of coordinates; reasoning over such terms has several practical applications, such as bus routes planning. Representing and reasoning on trajectories is a specific case of qualitative spatial reasoning that focuses on moving objects and their paths. In this work, we propose two versions of a trajectory calculus based on the allowed properties over trajectories, where trajectories are defined as a sequence of non-overlapping regions of a partitioned map. More specifically, if a given trajectory is allowed to start and finish at the same region, 6 base relations are defined (TC-6). If a given trajectory should have different start and finish regions but cycles are allowed within, 10 base relations are defined (TC-10). Both versions of the calculus are implemented as ASP programs; we propose several different encodings, including a generalised program capable of encoding any qualitative calculus in ASP. All proposed encodings are experimentally evaluated using a real-world dataset. Experiment results show that the best performing implementation can scale up to an input of 250 trajectories for TC-6 and 150 trajectories for TC-10 for the problem of discovering a consistent configuration, a significant improvement compared to previous ASP implementations for similar qualitative spatial and temporal calculi. This manuscript is under consideration for acceptance in TPLP.

This paper tackles the problem of evaluating the degree of inconsistency in spatial and temporal qualitative reasoning. We first introduce postulates to propose a formal framework for measuring inconsistency in this context. Then, we provide two inconsistency measures that can be useful in various AI applications. The first one is based on the number of constraints that we need to relax to get a consistent qualitative constraint network. The second inconsistency measure is based on variable restrictions to restore consistency. It is defined from the minimum number of variables that we need to ignore to recover consistency. We show that our proposed measures satisfy required postulates and other appropriate properties. Finally, we discuss the impact of our inconsistency measures on belief merging in qualitative reasoning.

In this paper, we focus on a recently introduced problem in the context of spatial and temporal qualitative reasoning, called the MAX-QCN problem. This problem involves obtaining a spatial or temporal configuration that maximizes the number of satisfied constraints in a qualitative constraint network (QCN). To efficiently solve the MAX-QCN problem, we introduce and study two families of encodings of the partial maximum satisfiability problem (PMAX-SAT). Each ofthese encodings is based on, what we call, a forbidden covering with regard to the composition table of the considered qualitative calculus. Intuitively, a forbidden covering allows us to express, in a more or less compact manner, the non-feasible configurations for three spatial or temporal entities.The experimentation that we have conducted with qualitative constraint networks from the Interval Algebra shows the interest of our approach.

RCC8 is a constraint language that serves for qualitative spatial representation and reasoning by encoding the topological relations between spatial entities. We focus on efficiently characterizing non-redundant constraints in large real world RCC8 networks and obtaining their prime networks. For a RCC8 network N a constraint is redundant, if removing that constraint from N does not change the solution set of N. A prime network of N is a network which contains no redundant constraints, but has the same solution set as N. We make use of a particular partial consistency, namely, G-path consistency, and obtain new complexity results for various cases of RCC8 networks, while we also show that given a maximal distributive subclass for RCC8 and a network N defined on that subclass, the prunning capacity of G-path consistency and path consistency is identical on the common edges of G and the complete graph of N, when G is a triangulation of the constraint graph of N. Finally, we devise an algorithm based on G-path consistency to compute the unique prime network of a RCC8 network, and show that it significantly progresses the state-of-the-art for practical reasoning with real RCC8 networks scaling up to millions of nodes.

The fundamental reasoning problem in RCC-8 is deciding In contrast to the synthetic RCC-8 networks that have the consistency of a set of constraints Θ, i.e., whether there been used in the literature for evaluating the aforementioned is a spatial configuration where the relations between the reasoners, the real-world networks of Table 1 are very sparse regions can be described by Θ. Traditionally in qualitative and one to two orders of magnitude larger. The labels on spatial reasoning (QSR) consistency of such sets is decided their edges contain 1 or 2 base RCC-8 relations forming a by a backtracking algorithm which optionally uses a pathconsistency disjunction. This kind of networks have not been employed algorithm as a preprocessing step for forward in any experimental evaluation of RCC-8 reasoners with the checking. In general, this problem is NPcomplete (Renz exception of (Sioutis and Koubarakis 2012) in which the network and Nebel 1999). However it has been shown in (Renz 1999) adm1 has been used. Typically, the literature focuses that there are tractable subsets of RCC-8 for which the consistency on quite smaller networks (20 to 1000 nodes) with an average problem can be decided by path-consistency. of 4 base RCC-8 relations per edge, and an average Table 1 depicts the characteristics of some real-world node degree ranging from 4 to 20. Deciding the consistency RCC-8 networks recording the topological relations between of real-world networks is a very important task. Inconsistencies administrative regions in Europe (networks nuts, might arise because their RCC-8 relations are computed adm1, and adm2) and the world (networks gadm1 and based on the geometries of geographical objects which gadm2), and the performance of the following reasoners often have not been captured correctly (e.g., overlapping geometries regarding consistency checking: Renz-Nebel01 (Renz and between two regions that in principle are externally Nebel 2001), GQR-1500 (Gantner, Westphal, and Woelfl connected). This is the case for the networks gadm1 and 2008; Westphal and Hué 2012), PPyRCC8 (Sioutis and gadm2.