We provide a comprehensive elaboration of the theoretical foundations of variable instantiation, or grounding, in Answer Set Programming (ASP). Building on the semantics of ASP's modeling language, we introduce a formal characterization of grounding algorithms in terms of (fixed point) operators. A major role is played by dedicated well-founded operators whose associated models provide semantic guidance for delineating the result of grounding along with on-the-fly simplifications. We address an expressive class of logic programs that incorporates recursive aggregates and thus amounts to the scope of existing ASP modeling languages. This is accompanied with a plain algorithmic framework detailing the grounding of recursive aggregates. The given algorithms correspond essentially to the ones used in the ASP grounder gringo.
Answer set programming is a declarative logic programming paradigm geared towards solving difficult combinatorial search problems. While different logic programs can encode the same problem, their performance may vary significantly. It is not always easy to identify which version of the program performs the best. We present the system Predictor (and its algorithmic backend) for estimating the grounding size of programs, a metric that can influence a performance of a system processing a program. We evaluate the impact of Predictor when used as a guide for rewritings produced by the answer set p…
In many probabilistic first-order representation systems, inference is performed by "grounding"---i.e., mapping it to a propositional representation, and then performing propositional inference. With a large database of facts, groundings can be very large, making inference and learning computationally expensive. Here we present a first-order probabilistic language which is well-suited to approximate "local" grounding: every query $Q$ can be approximately grounded with a small graph. The language is an extension of stochastic logic programs where inference is performed by a variant of personali…
National ICT Australia（澳大利亚国家信息与通信技术研究院）；University of Melbourne（墨尔本大学）
To appear in Theory and Practice of Logic Programming (TPLP) Bound Founded Answer Set Programming (BFASP) is an extension of Answer Set Programming (ASP) that extends stable model semantics to numeric variables. While the theory of BFASP is defined on ground rules, in practice BFASP programs are written as complex non-ground expressions. Flattening of BFASP is a technique used to simplify arbitrary expressions of the language to a small and well defined set of primitive expressions. In this paper, we first show how we can flatten arbitrary BFASP rule expressions, to give equivalent BFASP progr…
Cardiff University（卡迪夫大学）；Samsung AI Research（三星人工智能研究院）
State-of-the-art inference approaches in probabilistic logic programming typically start by computing the relevant ground program with respect to the queries of interest, and then use this program for probabilistic inference using knowledge compilation and weighted model counting. We propose an alternative approach that uses efficient Datalog techniques to integrate knowledge compilation with forward reasoning with a non-ground program. This effectively eliminates the grounding bottleneck that so far has prohibited the application of probabilistic logic programming in query answering scenarios…
National ICT Australia（澳大利亚国家信息与通信技术公司）；The University of Melbourne（墨尔本大学）
Answer Set Programming (ASP) is a powerful modelling formalism that is very efficient in solving combinatorial problems. ASP solvers implement the stable model semantics that eliminates circular derivations between Boolean variables from the solutions of a logic program. Due to this, ASP solvers are better suited than propositional satisfiability (SAT) and Constraint Programming (CP) solvers to solve a certain class of problems whose specification includes inductive definitions such as reachability in a graph. On the other hand, ASP solvers suffer from the grounding bottleneck that occurs due …
Repeated executions of reasoning tasks for varying inputs are necessary in many applicative settings, such as stream reasoning. In this context, we propose an incremental grounding approach for the answer set semantics. We focus on the possibility of generating incrementally larger ground logic programs equivalent to a given non-ground one; so called overgrounded programs can be reused in combination with deliberately many different sets of inputs. Updating overgrounded programs requires a small effort, thus making the instantiation of logic programs considerably faster when grounding is repea…
Siemens AG Österreich（奥地利西门子股份公司）；Alpen-Adria-Universität Klagenfurt（克拉根福阿尔卑斯-亚德里亚大学）；TU Wien（维也纳工业大学）
Domain-specific heuristics are an essential technique for solving combinatorial problems efficiently. Current approaches to integrate domain-specific heuristics with Answer Set Programming (ASP) are unsatisfactory when dealing with heuristics that are specified non-monotonically on the basis of partial assignments. Such heuristics frequently occur in practice, for example, when picking an item that has not yet been placed in bin packing. Therefore, we present novel syntax and semantics for declarative specifications of domain-specific heuristics in ASP. Our approach supports heuristic statemen…
Finite Groundings for ASP with Functions: A Journey through Consistency
Knowledge-Based Systems Group, TU Dresden（图腾大学达姆施塔特分校知识系统小组）；LIRMM, Inria, University of Montpellier, CNRS（里尔毫米研究所、法国国家信息与自动化技术研究院、蒙彼利埃大学、国家科学研究中心）；Massachusetts Institute of Technology（麻省理工学院）
ASP-based Multi-shot Reasoning via DLV2 with Incremental Grounding
University of Calabria（卡布里亚大学）；Gruppo Nazionale Calcolo Scientifico-Istituto Nazionale di Alta Matematica（国家科学计算集团-国家高级数学研究所）；DLVSystem Srl（DLVSystem公司）
Aalto University（阿尔托大学）；HIIT（赫尔辛基信息技术研究所）；University of Potsdam（波茨坦大学）；Simon Fraser University（西蒙弗雷泽大学）；IIIS Griffith University（格里菲斯大学综合智能系统研究所）；INRIA Rennes（法国国家信息与自动化研究所雷恩分所）
Problem solving in Answer Set Programming consists of two steps, a first grounding phase, systematically replacing all variables by terms, and a second solving phase computing the stable models of the obtained ground program. An intricate part of both phases is the treatment of aggregates, which are popular language constructs that allow for expressing properties over sets. In this paper, we elaborate upon the treatment of aggregates during grounding in Gringo series 4. Consequently, our approach is applicable to grounding based on semi-naive database evaluation techniques. In particular, we p…
Siemens AG Österreich（西门子奥地利公司）；Alpen-Adria-Universität Klagenfurt（克拉根福阿尔卑斯-亚德里亚大学）；Technische Universität Wien（维也纳工业大学）
The traditional ground-and-solve approach to Answer Set Programming (ASP) suffers from the grounding bottleneck, which makes large-scale problem instances unsolvable. Lazy grounding is an alternative approach that interleaves grounding with solving and thus uses space more efficiently. The limited view on the search space in lazy grounding poses unique challenges, however, and can have adverse effects on solving performance. In this paper we present a novel characterization of degrees of laziness in grounding for ASP, i.e. of compromises between lazily grounding as little as possible and the t…
University of Potsdam（波茨坦大学）；INRIA Rennes（雷恩国家信息与自动化研究所）；School of Computing Science at Simon Fraser University（西蒙弗雷泽大学计算科学学院）；Institute for Integrated and Intelligent Systems at Griffith University（格里菲斯大学集成与智能系统研究所）
We introduce a new flexible paradigm of grounding and solving in Answer Set Programming (ASP), which we refer to as multi-shot ASP solving, and present its implementation in the ASP system clingo. Multi-shot ASP solving features grounding and solving processes that deal with continuously changing logic programs. In doing so, they remain operative and accommodate changes in a seamless way. For instance, such processes allow for advanced forms of search, as in optimization or theory solving, or interaction with an environment, as in robotics or query-answering. Common to them is that the problem…
TU Wien (Vienna University of Technology)（维也纳工业大学）；Siemens AG Österreich（奥地利西门子公司）
Answer-Set Programming (ASP) is a powerful and expressive knowledge representation paradigm with a significant number of applications in logic-based AI. The traditional ground-and-solve approach, however, requires ASP programs to be grounded upfront and thus suffers from the so-called grounding bottleneck (i.e., ASP programs easily exhaust all available memory and thus become unsolvable). As a remedy, lazy-grounding ASP solvers have been developed, but many state-of-the-art techniques for grounded ASP solving have not been available to them yet. In this work we present, for the first time, ada…
University of Bremen（不来梅大学）；The DesignSpace Group（设计空间小组）；MANIT（迈普尔国家理工学院）
We present a general theory and corresponding declarative model for the embodied grounding and natural language based analytical summarisation of dynamic visuo-spatial imagery. The declarative model ---ecompassing spatio-linguistic abstractions, image schemas, and a spatio-temporal feature based language generator--- is modularly implemented within Constraint Logic Programming (CLP). The implemented model is such that primitives of the theory, e.g., pertaining to space and motion, image schemata, are available as first-class objects with `deep semantics' suited for inference and query. We demo…
Software testing is a prime factor in software industry. Besides knowing the importance of testing, only limited time is allocated for teaching it. It will be more efficient if testing is taught simultaneously with programming foundations. This integrated learning of testing techniques and programming allows the programmers to perform in a better way and this leads to the improvement of the performance of the industry progress. In this paper, a technique named ontology is introduced, it first defines the various testing process in hierarchy and define relationships among them, to share and reu…
A. Becker University of Potsdam, Germany（波恩大学）；P. Cabalar University of Corunna, Spain（科鲁纳大学）；M. Diéguez LERIA, University of Angers, France（安格里大学）；J. Romero University of Potsdam, Germany（波恩大学）；S. Hahn University of Potsdam, Germany（波恩大学）；T. Schaub University of Potsdam, Germany（波恩大学）
本文提出一种编译度量时序 ASP 的方法，借助 difference constraints 在外部处理时间约束，以缓解细粒度时间导致的 grounding 瓶颈，并使求解不受时间精度影响。
GroundingPI: A Grounding Foundation Model towards Physical Intelligence with Visual Primitives
XPeng Inc.（小鹏汽车）；Peking University（北京大学）；The University of Hong Kong（香港大学）；University of California, Berkeley（加州大学伯克利分校）；Princeton University（普林斯顿大学）；National University of Singapore（新加坡国立大学）；Tsinghua University（清华大学）；HKUST (GZ)（香港科技大学（广州））
It is argued that 4-valued paraconsistent truth values (called here "p-bits") can serve as a conceptual, mathematical and practical foundation for highly AI-relevant forms of probabilistic logic and probabilistic programming and concept formation. First it is shown that appropriate averaging-across-situations and renormalization of 4-valued p-bits operating in accordance with Constructible Duality (CD) logic yields PLN (Probabilistic Logic Networks) strength-and-confidence truth values. Then variations on the Curry-Howard correspondence are used to map these paraconsistent and probabilistic lo…
GG-Nav: Goal-oriented Grounding Chain-of-Thought Elicits Reasoning in Navigation Foundation Models
Zhejiang University（浙江大学）；Shanghai AI Laboratory（上海人工智能实验室）；University of Science and Technology of China（中国科学技术大学）；Tongji University（同济大学）；The University of Hong Kong（香港大学）
An analysis of HOI: using a training-free method with multimodal visual foundation models when only the test set is available, without the training set
JEPA-DNA: Grounding Genomic Foundation Models through Joint-Embedding Predictive Architectures
Applied AI Architecture, NVIDIA, Israel（NVIDIA应用人工智能架构，以色列）；Worldwide Field Ops, NVIDIA, Israel（NVIDIA全球现场运营，以色列）；Developer Programs, NVIDIA, Israel（NVIDIA开发者计划，以色列）；Cancer Research Center and Wohl Institute of Translational Medicine, Sheba Medical Center, Tel Hashomer, Israel（癌症研究中心和Wohl转化医学研究所，Sheba医疗中心，Tel Hashomer，以色列）；Windreich Department of AI and Human Health, Icahn School of Medicine at Mount Sinai, New York, USA（AI与人类健康风reich部门，Mount Sinai医学中心，纽约，美国）
Spoken language is a convenient interface for commanding a mobile robot. Yet for this to work a number of base terms must be grounded in perceptual and motor skills. We detail the language processing used on our robot ELI and explain how this grounding is performed, how it interacts with user gestures, and how it handles phenomena such as anaphora. More importantly, however, there are certain concepts which the robot cannot be preprogrammed with, such as the names of various objects in a household or the nature of specific tasks it may be requested to perform. In these cases it is vital that t…
Trifuse: Enhancing Attention-Based GUI Grounding via Multimodal Fusion
College of Computer Science and Technology, National University of Defense Technology（计算机科学与技术学院，国防科技大学）；Intelligent Game and Decision Lab, Academy of Military Sciences（智能游戏与决策实验室，军事科学院）
Toward AI-Driven Digital Organism: Multiscale Foundation Models for Predicting, Simulating and Programming Biology at All Levels
GenBio AI；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Weizmann Institute of Science（魏茨曼科学研究所）；Carnegie Mellon University（卡内基梅隆大学）
GRAIL: Autonomous Concept Grounding for Neuro-Symbolic Reinforcement Learning
Technical University of Darmstadt（达姆施塔特技术大学）；Google Intrinsic（谷歌内在）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；Centre for Cognitive Science, TU Darmstadt（达姆施塔特技术大学认知科学中心）
Probabilistic programming is related to a compositional approach to stochastic modeling by switching from discrete to continuous time dynamics. In continuous time, an operator-algebra semantics is available in which processes proceeding in parallel (and possibly interacting) have summed time-evolution operators. From this foundation, algorithms for simulation, inference and model reduction may be systematically derived. The useful consequences are potentially far-reaching in computational science, machine learning and beyond. Hybrid compositional stochastic modeling/probabilistic programming a…
VideoGEM: Training-free Action Grounding in Videos
Goethe University Frankfurt（法兰克福大学）；Tuebingen AI Center（蒂宾根人工智能中心）；University of Tuebingen（蒂宾根大学）；MPI for Informatics（马克斯·普朗克信息学研究所）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）
