Author name cluster

Ingo Pill

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5 papers

2 author rows

AAAI Conference 2020 Conference Paper

Efficient Model-Based Diagnosis of Sequential Circuits

Alexander Feldman
Ingo Pill
Franza Wotawa
Ion Matei
Johan de Kleer

In Model-Based Diagnosis (MBD), we concern ourselves with the health and safety of physical and software systems. Although we often use different knowledge representations and algorithms, some tools like satisﬁability (SAT) solvers and temporal logics, are used in both domains. In this paper we introduce Finite Trace Next Logic (FTNL) models of sequential circuits and propose an enhanced algorithm for computing minimal-cardinality diagnoses. Existing state-of-the-art satisﬁability algorithms for minimal diagnosis use Sorting Networks (SNs) for constraining the cardinality of the diagnostic candidates. In our approach we exploit Multi-Operand Adders (MOAs). Based on extensive tests with ISCAS-89 circuits, we found that MOAs enable Conjunctive Normal Form (CNF) encodings that are signiﬁcantly more compact. These encodings lead to 19. 7 to 67. 6 times fewer variables and 18. 4 to 62 times fewer clauses. For converting an FTNL model to CNF, we could achieve a speed-up ranging from 6. 2 to 22. 2. Using SNs fosters 3. 4 to 5. 5 times faster on-line satisﬁability checking though. This makes MOAs preferable for applications where RAM and off-line time are more limited than on-line CPU time.

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IJCAI Conference 2013 Conference Paper

Behavioral Diagnosis of LTL Specifications at Operator Level

Ingo Pill
Thomas Quaritsch

Product defects and rework efforts due to flawed specifications represent major issues for a project’s performance, so that there is a high motivation for providing effective means that assist designers in assessing and ensuring a specification’s quality. Recent research in the context of formal specifications, e. g. on coverage and vacuity, offers important means to tackle related issues. In the currently underrepresented research direction of diagnostic reasoning on a specification, we propose a scenario-based diagnosis at a specification’s operator level using weak or strong fault models. Drawing on efficient SAT encodings, we show in this paper how to achieve that effectively for specifications in LTL. Our experimental results illustrate our approach’s validity and attractiveness.

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IJCAI Conference 2013 Conference Paper

The Route to Success – A Performance Comparison of Diagnosis Algorithms

Iulia Nica
Ingo Pill
Thomas Quaritsch
Franz Wotawa

Diagnosis, i. e. , the identification of root causes for failing or unexpected system behavior, is an important task in practice. Within the last three decades, many different AI-based solutions for solving the diagnosis problem have been presented and have been gaining in attraction. This leaves us with the question of which algorithm to prefer in a certain situation. In this paper we contribute to answering this question. In particular, we compare two classes of diagnosis algorithms. One class exploits conflicts in their search, i. e. , sets of system components whose correct behavior contradicts given observations. The other class ignores conflicts and derives diagnoses from observations and the underlying model directly. In our study we use different reasoning engines ranging from an optimized Horn-clause theorem prover to general SAT and constraint solvers. Thus we also address the question whether publicly available general reasoning engines can be used for an efficient diagnosis.

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ECAI Conference 2012 Conference Paper

Optimizations for the Boolean Approach to Computing Minimal Hitting Sets

Ingo Pill
Thomas Quaritsch

The Boolean approach to computing minimal hitting sets proposed by Lin and Jiang is known to offer very attractive general performance, but also has its issues, specifically with a cardinality-restricted search. In this paper we propose optimizations regarding the refinement rules, also offering a revised decision strategy as well as optimized termination criteria that exploit cardinality bounds. Our experiments including artificial and real-world samples for the bounded and unbounded case show the potential of our work, where we could achieve speed-ups of up to two orders of magnitude.

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IJCAI Conference 2011 Conference Paper

Belief Management for High-Level Robot Programs

Stephan Gspandl
Ingo Pill
Michael Reip
Gerald Steinbauer
Alexander Ferrein

The robot programming and plan language IndiGolog allows for on-line execution of actions and offline projections of programs in dynamic and partly unknown environments. Basic assumptions are that the outcomes of primitive and sensing actions are correctly modeled, and that the agent is informed about all exogenous events beyond its control. In real-world applications, however, such assumptions do not hold. In fact, an action's outcome is error-prone and sensing results are noisy. In this paper, we present a belief management system in IndiGolog that is able to detect inconsistencies between a robot's modeled belief and what happened in reality. The system furthermore derives explanations and maintains a consistent belief. Our main contributions are (1) a belief management system following a history-based diagnosis approach that allows an agent to actively cope with faulty actions and the occurrence of exogenous events; and (2) an implementation in IndiGolog and experimental results from a delivery domain.

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