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Polynomial Formal Verification of Approximate Adders with Constant Cutwidth 29

Polynomial Formal Verification of Approximate Adders with Co...

引用

IEEE European Test Symposium (ETS)

作者： Nadeem, Mohamed Jha, Chandan Kumar Drechsler, Rolf Univ Bremen Bremen Germany Univ Bremen DFKI Bremen Germany

ISBN: (纸本)9798350349320;9798350349337

In the context of digital circuits, formal verification methods have been well-studied to ensure their functional correctness. However, several verification methods fail to provide an upper bound for the time and space complexity. Therefore, Polynomial Formal Verification (PFV) has been introduced to address this problem. Unlike prior works, which have shown that approximate circuits can be verified in polynomial time, we show that approximate circuits with a constant cutwidth can be verified even in linear time. Since approximate circuits have become ubiquitous in error-resilient applications, it becomes essential to guarantee their correctness. While prior works have been limited to formal error analysis, we use answer set programming (ASP) based formal verification to guarantee that the approximate circuit matches its functional specification. In this paper, we first show that several approximate adder circuits exhibit a constant cutwidth. We then provide a PFV approach that relies on this cutwidth as a structural property of the circuits to guarantee a linear-time verification w.r.t. the bitwidth using ASP. Finally, we evaluate several approximate adders in terms of the upper bound of the cutwidth, and verification time.

关键词： Polynomial Formal Verification Logic Synthesis Approximate Circuits Dynamic programming answer set programming Cutwidth

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Short Boolean Formulas as Explanations in Practice 18th

Short Boolean Formulas as Explanations in Practice

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18th European Conference on Logics in Artificial Intelligence (JELIA)

作者： Jaakkola, Reijo Janhunen, Tomi Kuusisto, Antti Rankooh, Masood Feyzbakhsh Vilander, Miikka Tampere Univ Tampere Finland Univ Helsinki Helsinki Finland

ISBN: (纸本)9783031436185;9783031436192

We investigate explainability via short Boolean formulas in the data model based on unary relations. As an explanation of length k, we take a Boolean formula of length k that minimizes the error with respect to the target attribute to be explained. We first provide novel quantitative bounds for the expected error in this scenario. We then also demonstrate how the setting works in practice by studying three concrete data sets. In each case, we calculate explanation formulas of different lengths using an encoding in answer set programming. The most accurate formulas we obtain achieve errors similar to other methods on the same data sets. However, due to overfitting, these formulas are not necessarily ideal explanations, so we use cross validation to identify a suitable length for explanations. By limiting to shorter formulas, we obtain explanations that avoid overfitting but are still reasonably accurate and also, importantly, human interpretable.

关键词： Boolean formula size Explainability Interpretable AI Overfitting error answer set programming Boolean optimization

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Stable Model Semantics for Recursive SHACL 20

Stable Model Semantics for Recursive SHACL

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29th Web Conference (WWW)

作者： Andresel, Medina Corman, Julien Ortiz, Magdalena Reutter, Juan L. Savkovic, Ognjen Simkus, Mantas TU Wien Vienna Austria Free Univ Bozen Bolzano Bolzano Italy PUC Chile Santiago Chile IMFD Chile Santiago Chile

ISBN: (纸本)9781450370233

SHACL (SHape Constraint Language) is a W3C recommendation for validating graph-based data against a set of constraints (called shapes). Importantly, SHACL allows to define recursive shapes, i.e. a shape may refer to itself, directly of indirectly. The recommendation left open the semantics of recursive shapes, but proposals have emerged recently to extend the official semantics to support recursion. These proposals are based on the principle of possibility (or non-contradiction): a graph is considered valid against a schema if one can assign shapes to nodes in such a way that all constraints are satisfied. This semantics is not constructive, as it does not provide guidelines about how to obtain such an assignment, and it may lead to unfounded assignments, where the only reason to assign a shape to a node is that it allows validating the graph. In contrast, we propose in this paper a stricter, more constructive semantics for SHACL, based on stable models, which are well-known in answer set programming (ASP). This semantics additionally requires a shape assignment to be properly justified by the input constraints. We further exploit the connection to logic programming, and show that SHACL constraints can be naturally represented as logic programs, and that the validation problem for a graph and a SHACL schema can be encoded as an ASP reasoning task. The proposed semantics also enjoys computationally tractable validation in the presence of constraints with stratified negation (as opposed to the previous semantics). We also extend our semantics to 3-valued stable models, which yields a more relaxed notion of validation, tolerant to certain faults in the schema or data. By exploiting a connection between 3-valued stable model semantics and the well-founded semantics for logic programs, we can use our translation into ASP to show another tractability result. Finally, we provide a preliminary evaluation of the approach, which leverages an ASP solver to perform graph valida

关键词： SHACL graph-structured data answer set programming

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Multi-Agent Planning and Diagnosis with Commonsense Reasoning 23

Multi-Agent Planning and Diagnosis with Commonsense Reasonin...

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5th International Conference on Distributed Artificial Intelligence (DAI)

作者： Son, Tran Cao Yeoh, William Stern, Roni Kalech, Meir New Mexico State Univ Las Cruces NM 88003 USA Washington Univ St Louis St Louis MO USA Bengurion Univ Negev Beer Sheva Israel

ISBN: (纸本)9798400708480

In multi-agent systems, multi-agent planning and diagnosis are two key subfields - multi-agent planning approaches identify plans for the agents to execute in order to reach their goals, and multiagent diagnosis approaches identify root causes for faults when they occur, typically by using information from the multi-agent planning model as well as the resulting multi-agent plan. However, when a plan fails during execution, the cause can often be related to some commonsense information that is neither explicitly encoded in the planning nor diagnosis problems. As such existing diagnosis approaches fail to accurately identify the root causes in such situations. To remedy this limitation, we extend the Multi-Agent STRIPS problem (a common multi-agent planning framework) to a Commonsense Multi-Agent STRIPS model, which includes commonsense fluents and axioms that may affect the classical planning problem. We show that a solution to a (classical) Multi-Agent STRIPS problem is also a solution to the commonsense variant of the same problem. Then, we propose a decentralized multi-agent diagnosis algorithm, which uses the commonsense information to diagnose faults when they occur during execution. Finally, we demonstrate the feasibility and promise of this approach on several key multiagent planning benchmarks.

关键词： Multi-Agent Systems Multi-Agent Planning Multi-Agent Diagnosis Commonsense Reasoning Decentralized Algorithms answer set programming

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A Declarative Approach to Contrast Pattern Mining 1

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21st International Conference of the Italian-Association-for-Artificial-Intelligence (AIxIA)

作者： Lisi, Francesca Alessandra Sterlicchio, Gioacchino Univ Bari Aldo Moro Dipartimento Informat Bari Italy Univ Bari Aldo Moro CILA Bari Italy Polytech Univ Bari Dept Mech Math & Management Bari Italy

This paper proposes a declarative approach to the problem of contrast pattern mining. The approach is based on encodings of the data and the problem with answer set programming (ASP), and evaluated in a novel AI appli... 详细信息

ISBN: (数字)9783031271816

ISBN: (纸本)9783031271809;9783031271816

关键词： Contrast Pattern Mining answer set programming Digital Forensics

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Extended Knowledge Graphs: A Conceptual Study 12

Extended Knowledge Graphs: A Conceptual Study

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12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) / 12th International Conference on Knowledge Engineering and Ontology Development (KEOD)

作者： Adrian, Weronika T. Adrian, Marek Kluza, Krzysztof Stachura-Terlecka, Bernadetta Ligeza, Antoni AGH Univ Sci & Technol Al A Mickiewicza 30 PL-30059 Krakow Poland

ISBN: (纸本)9789897584749

The amount and variety of data that we produce every day pose a constant challenge for meaningful information processing. While knowledge graphs have gained a considerable attention in the recent years, due to their flexible and universal knowledge representation, they lack the mechanisms facilitating knowledge processing. In this paper, we propose an information system called extended knowledge graphs (EKG) that augments the concept of knowledge graphs with procedural attachments. We put forward the requirements and assumption of the EKG structure and present a categorisation of supported reasoning tasks.

关键词： Knowledge Graphs Semantic Networks Knowledge Representation KRR Rules Ontologies Reasoning Logic programming answer set programming Prolog

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Inductive Learning Using Constraint-Driven Bias 1

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24th International Conference on Inductive Logic programming (ILP)

作者： Athakravi, Duangtida Alrajeh, Dalal Broda, Krysia Russo, Alessandra Satoh, Ken Univ London Imperial Coll Sci Technol & Med London England Natl Inst Informat Tokyo Japan

ISBN: (数字)9783319237084

ISBN: (纸本)9783319237084;9783319237077

Heuristics such as the Occam Razor's principle have played a significant role in reducing the search for solutions of a learning task, by giving preference to most compressed hypotheses. For some application domains, however, these heuristics may become too weak and lead to solutions that are irrelevant or inapplicable. This is particularly the case when hypotheses ought to conform, within the scope of a given language bias, to precise domain-dependent structures. In this paper we introduce a notion of inductive learning through constraint-driven bias that addresses this problem. Specifically, we propose a notion of learning task in which the hypothesis space, induced by its mode declaration, is further constrained by domain-specific denials, and acceptable hypotheses are (brave inductive) solutions that conform with the given domain-specific constraints. We provide an implementation of this new learning task by extending the ASPAL learning approach and leveraging on its meta-level representation of hypothesis space to compute acceptable hypotheses. We demonstrate the usefulness of this new notion of learning by applying it to two class of problems - automated revision of software system goals models and learning of stratified normal programs.

关键词： Inductive logic programming answer set programming Meta-level constraints

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Solving Problems in the Polynomial Hierarchy with ASP(Q) 16th

Solving Problems in the Polynomial Hierarchy with ASP(Q)

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16th International Conference on Logic programming and Non-Monotonic Reasoning (LPNMR)

作者： Amendola, Giovanni Cuteri, Bernardo Ricca, Francesco Truszczynski, Mirek Univ Calabria Arcavacata Di Rende Italy Univ Kentuky Lexington KY USA

ISBN: (纸本)9783031157073;9783031157066

answer set programming with Quantifiers ASP(Q) is a recent extension of answer set programming (ASP) that allows one to model problems from the entire polynomial hierarchy. Earlier work focused on demonstrating modeling capabilities of ASP(Q). In this paper, we propose a modular ASP(Q) solver that translates a quantified ASP program together with a given data instance into a Quantified Boolean Formula (QBF) to be solved by any QBF solver. We evaluate the performance of our solver on several instances and with different back-end QBF solvers, demonstrating the efficacy of ASP(Q) as a tool for rapid modeling and solving of complex combinatorial problems. The benchmark problems we use include two new ones, Argumentation Coherence and Para-coherent ASP, for which we develop elegant ASP(Q) encodings.

关键词： answer set programming Quantifiers QBF

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From Sequential Algorithm Selection to Parallel Portfolio Selection 1

引用

9th International Conference on Learning and Intelligent OptimizatioN (LION)

作者： Lindauer, M. Hoos, Holger H. Hutter, F. Univ Freiburg D-79106 Freiburg Germany Univ British Columbia Vancouver BC V5Z 1M9 Canada

ISBN: (数字)9783319190846

ISBN: (纸本)9783319190846;9783319190839

In view of the increasing importance of hardware parallelism, a natural extension of per-instance algorithm selection is to select a set of algorithms to be run in parallel on a given problem instance, based on features of that instance. Here, we explore how existing algorithm selection techniques can be effectively parallelized. To this end, we leverage the machine learning models used by existing sequential algorithm selectors, such as 3S, ISAC, SATzilla and ME-ASP, and modify their selection procedures to produce a ranking of the given candidate algorithms;we then select the top n algorithms under this ranking to be run in parallel on n processing units. Furthermore, we adapt the pre-solving schedules obtained by aspeed to be effective in a parallel setting with different time budgets for each processing unit. Our empirical results demonstrate that, using 4 processing units, the best of our methods achieves a 12-fold average speedup over the best single solver on a broad set of challenging scenarios from the algorithm selection library.

关键词： Algorithm selection Parallel portfolios Constraint solving answer set programming

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THE MAASAI OF SOUTHERN KENYA DOMAIN MODEL OF LAND USE

THE MAASAI OF SOUTHERN KENYA DOMAIN MODEL OF LAND USE

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ISPRS TC IV Mid-Term Symposium on 3D Spatial Information Science - The Engine of Change

作者： Karamesouti, Mina Schultz, Carl Chipofya, Malumbo Jan, Sahib Galeano, Cristhian Eduardo Murcia Schwering, Angela Timm, Christian Univ Munster Inst Geoinformat Munster Germany Aarhus Univ DIGIT Aarhus Denmark Hansa Luftbild AG Munster Germany

We present a domain model that formalises the human-land relations in the Maasai nomadic pastoralist society in Kenya, referred to as MSKDM, and its integration with the prominent Land Administration Domain Model (LADM). Our long-term aim is to facilitate a land administration system that can accurately capture and express salient Maasai concepts of land use, ownership, communal tenure, and to assist in transparency during land transactions. We use an extensive corpus of existing research literature, and input from our own on-site workshops, as source material for our domain model. We use real sketch maps drawn by Maasai community members that we collected during our field studies for validation, and to demonstrate how our model can be operationalised.

关键词： Indigenous knowledge representation Land administration Land management answer set programming Sketchmaps

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