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Oliver Ray

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University of Cyprus

andrei doncescu

Université Paul Sabatier de Toulouse

University of Bristol

Ioannis Tambakis

University College London

Abdelghani Chibani

Université Paris-Est Créteil

Imperial College London

Universität Bremen

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Papers by Oliver Ray

Learning and Revising Dynamic Temporal Theories in the Full Discrete Event Calculus

Springer eBooks, 2022

This paper shows how a nonmonotonic ILP system XHAIL can perform general-purpose learning and rev... more This paper shows how a nonmonotonic ILP system XHAIL can perform general-purpose learning and revision of temporal theories in a full-fledged Discrete Event Calculus (DEC) framework with several features (now introduced into ILP for the first time) for dynamically releasing fluents from the commonsense law of inertia and subjecting them to mathematically-defined gradual change trajectories. First, we review key aspects in the development of the DEC and prior attempts aimed at learning within simple fragments of it. Then, we introduce a new formulation of DEC, called the eXploratory Event Calculus (XEC), which (a) results in significantly reduced grounding size and execution times with respect to state-of-the-art translations of DEC from circumscriptive logic into Answer Set Programming (ASP), and which (b) provides a unifying framework that supports different policies used (explicitly or implicitly) in prior work to resolve conflicts between concurrent events competing to determine the truth value and/or release status of fluents under a circumscriptive or answer set semantics. Finally, we give the first known demonstration of learning and revision of theories in the full DEC by using XHAIL and XEC in a proof-of-principle example showing how such theories can be revised under different conflict resolution policies.

CISIS IIBM Welcome Message

Advanced information processing technologies have the potential to significantly accelerate resea... more Advanced information processing technologies have the potential to significantly accelerate research in molecular biology and medicine. In particular, techniques from Artificial Intelligence, Machine Learning, and Data Mining can assist researchers in the discovery of new knowledge needed to deliver improved healthcare and personalized medicine. This workshop aims to attract state-of-the-art solutions and novel attempts in this direction.

Representation, Simulation, and Hypothesis Generation in Graph and Logical Models of Biological Networks

Methods in molecular biology, 2011

This chapter presents a discussion of metabolic modeling from graph theory and logical modeling p... more This chapter presents a discussion of metabolic modeling from graph theory and logical modeling perspectives. These perspectives are closely related and focus on the coarse structure of metabolism, rather than the finer details of system behavior. The models have been used as background knowledge for hypothesis generation by Robot Scientists using yeast as a model eukaryote, where experimentation and machine learning are used to identify additional knowledge to improve the metabolic model. The logical modeling concept is being adapted to cell signaling and transduction biological networks.

Nonmonotonic Learning in Large Biological Networks

Springer eBooks, 2015

This paper introduces a new open-source implementation of a nonmonotonic learning method called X... more This paper introduces a new open-source implementation of a nonmonotonic learning method called XHAIL and shows how it can be used for abductive and inductive inference on metabolic networks that are many times larger than could be handled by the preceding prototype. We summarise several implementation improvements that increase its efficiency and we introduce an extended form of language bias that further increases its usability. We investigate the system's scalability in a case study involving real data previously collected by a Robot Scientist and show how it led to the discovery of an error in a whole-organism model of yeast metabolism.

Logic-Based Steady-State Analysis and Revision of Metabolic Networks with Inhibition

This paper presents a qualitative logic-based method for the steady-state analysis and revision o... more This paper presents a qualitative logic-based method for the steady-state analysis and revision of metabolic networks with inhibition. The approach is able to automatically revise an initial metabolic model -through the addition and removal of whole reactions or individual substrates, products and inhibitors -in order to ensure the existence of a steady-state behaviour consistent with a set of experimental observations. We show how this can be done in a nonmonotonic logic programming setting and discuss the challenges that arise when metabolic cycles or mutual inhibitions occur in the underlying network.

A Neural Network Approach for First-Order Abductive Inference

This paper presents a neural network approach for first-order abductive inference by generalising... more This paper presents a neural network approach for first-order abductive inference by generalising an existing method from propositional logic to the first-order case. We show how the original propositional method can be extended to enable the grounding of a first-order abductive problem; and we also show how it can be modified to allow the prioritised computation of minimal solutions. We illustrate the approach on a well-known abductive problem and explain how it can be used to perform first-order conditional query answering.

Automatic Revision of Metabolic Networks through Logical Analysis of Experimental Data

Springer eBooks, 2010

This paper presents a nonmonotonic ILP approach for the automatic revision of metabolic networks ... more This paper presents a nonmonotonic ILP approach for the automatic revision of metabolic networks through the logical analysis of experimental data. The method extends previous work in two respects: by suggesting revisions that involve both the addition and removal of information; and by suggesting revisions that involve combinations of gene functions, enzyme inhibitions, and metabolic reactions. Our proposal is based on a new declarative model of metabolism expressed in a nonmonotonic logic programming formalism. With respect to this model, a mixture of abductive and inductive inference is used to compute a set of minimal revisions needed to make a given network consistent with some observed data. In this way, we describe how a reasoning system called XHAIL was able to correctly revise a state-of-the-art metabolic pathway in the light of real-world experimental data acquired by an autonomous laboratory platform called the Robot Scientist.

AstronomicAL: an interactive dashboard for visualisation, integration and classification of data with Active Learning

Journal of open source software, Sep 3, 2021

AstronomicAL is a human-in-the-loop interactive labelling and training dashboard that allows user... more AstronomicAL is a human-in-the-loop interactive labelling and training dashboard that allows users to create reliable datasets and robust classifiers using active learning. This technique prioritises data that offer high information gain, leading to improved performance using substantially less data. The system allows users to visualise and integrate data from different sources and deal with incorrect or missing labels and imbalanced class sizes. AstronomicAL enables experts to visualise domain-specific plots and key information relating both to broader context and details of a point of interest drawn from a variety of data sources, ensuring reliable labels. In addition, AstronomicAL provides functionality to explore all aspects of the training process, including custom models and query strategies. This makes the software a tool for experimenting with both domain-specific classifications and more general-purpose machine learning strategies. We illustrate using the system with an astronomical dataset due to the field's immediate need; however, AstronomicAL has been designed for datasets from any discipline. Finally, by exporting a simple configuration file, entire layouts, models, and assigned labels can be shared with the community. This allows for complete transparency and ensures that the process of reproducing results is effortless.

Towards a Logical Reconstruction of CF-Induction

Lecture Notes in Computer Science, 2008

CF-induction is a sound and complete hypothesis finding procedure for full clausal logic which us... more CF-induction is a sound and complete hypothesis finding procedure for full clausal logic which uses the principle of inverse entailment to compute a hypothesis that logically explains a set of examples with respect to a prior background theory. Currently, CF-induction computes hypotheses by applying combinations of several complex generalisation operators to an intermediate theory called a bridge formula. In this paper we propose an alternative approach whereby hypotheses are derived from a bridge formula using a single deductive operator and a single inductive operator. We show that our simplified procedure preserves the soundness and completeness of CF-induction.

Analyzing Pathways Using ASP-Based Approaches

Lecture Notes in Computer Science, 2012

This paper contributes to a line of research which aims to combine numerical information with log... more This paper contributes to a line of research which aims to combine numerical information with logical inference in order to find the most likely states of a biological system under various (actual or hypothetical) constraints. To this end, we build upon a state-of-the-art approach that employs weighted Boolean constraints to represent and reason about biochemical reaction networks. Our first contribution is to show how this existing method fails to deal satisfactorily with networks that contain cycles. Our second contribution is to define a new method which correctly handles such cases by exploiting the formalism of Answer Set Programming (ASP). We demonstrate the significance of our results on two case-studies previously studied in the literature.

Towards a Rational Approach for the Logical Modelling of Inhibition in Metabolic Networks

This paper makes two contributions towards the logical modelling of inhibition in metabolic netwo... more This paper makes two contributions towards the logical modelling of inhibition in metabolic networks. First it exposes the logical inconsistency of an existing state-of-the-art metabolic theory. Then it proposes a new model from which the required hypotheses are consistently inferred. Underlying this work is use of nonmonotonic logic programs to represent and reason about competing inhibitory effects on the net concentrations of metabolites in enzyme-catalysed biochemical networks.

The Need for Ancestor Resolution When Answering Queries in Horn Clause Logic

Springer eBooks, 2005

Prolog Technology Theorem Proving. (PTTP) [5] is a well known extension of Prolog for answering q... more Prolog Technology Theorem Proving. (PTTP) [5] is a well known extension of Prolog for answering queries in first-order logic. PTTP is based on the idea that 'Prolog can be viewed as an "almost complete" theorem prover, which has to be extended by only a few ingredients in order to handle the non-Horn case' . As explained in [5], PTTP is an efficient realisation of the Model Elimination (ME) calculus [2] that utilises five extensions of standard Prolog:

Nonmonotonic abductive inductive learning

Journal of Applied Logic, Sep 1, 2009

Inductive Logic Programming (ILP) is concerned with the task of generalising sets of positive and... more Inductive Logic Programming (ILP) is concerned with the task of generalising sets of positive and negative examples with respect to background knowledge expressed as logic programs. Negation as Failure (NAF) is a key feature of logic programming which provides a means for nonmonotonic commonsense reasoning under incomplete information. But, so far, most ILP research has been aimed at Horn programs which exclude NAF, and has failed to exploit the full potential of normal programs that allow NAF. By contrast, Abductive Logic Programming (ALP), a related task concerned with explaining observations with respect to a prior theory, has been well studied and applied in the context of normal logic programs. This paper shows how ALP can be used to provide a semantics and proof procedure for nonmonotonic ILP that utilises practical methods of language and search bias to reduce the search space. This is done by lifting an existing method called Hybrid Abductive Inductive Learning (HAIL) from Horn clauses to normal logic programs. To demonstrate its potential benefits, the resulting system, called XHAIL, is applied to a process modelling case study involving a nonmonotonic temporal Event Calculus (EC).

Automatically Tuning the GCC Compiler to Optimize the Performance of Applications Running on the ARM Cortex-M3

arXiv (Cornell University), Feb 23, 2017

This paper introduces a novel method for automatically tuning the selection of compiler flags to ... more This paper introduces a novel method for automatically tuning the selection of compiler flags to optimize the performance of software intended to run on embedded hardware platforms. We begin by developing our approach on code compiled by the GNU C Compiler (GCC) for the ARM Cortex-M3 (CM3) processor; and we show how our method outperforms the industry standard -O3 optimization level across a diverse embedded benchmark suite. First we quantify the potential gains by using existing iterative compilation approaches that time-intensively search for optimal configurations for each benchmark. Then we adapt iterative compilation to output a single configuration that optimizes performance across the entire benchmark suite. Although this is a time-consuming process, our approach constructs an optimized variation of -O3, which we call -Ocm3, that realizes nearly two thirds of known available gains on the CM3 and significantly outperforms a more complex state-ofthe-art predictive method in cross-validation experiments. Finally, we demonstrate our method on additional platforms by constructing two more optimization levels that find even more significant speed-ups on the ARM Cortex-A8 and 8-bit AVR processors.

Automatically Tuning the GCC Compiler to Optimize the Performance of Applications Running on Embedded Systems

arXiv (Cornell University), Feb 23, 2017

This paper introduces a novel method for automatically tuning the selection of compiler flags to ... more This paper introduces a novel method for automatically tuning the selection of compiler flags to optimize the performance of software intended to run on embedded hardware platforms. We begin by developing our approach on code compiled by the GNU C Compiler (GCC) for the ARM Cortex-M3 (CM3) processor; and we show how our method outperforms the industry standard -O3 optimization level across a diverse embedded benchmark suite. First we quantify the potential gains by using existing iterative compilation approaches that time-intensively search for optimal configurations for each benchmark. Then we adapt iterative compilation to output a single configuration that optimizes performance across the entire benchmark suite. Although this is a time-consuming process, our approach constructs an optimized variation of -O3, which we call -Ocm3, that realizes nearly two thirds of known available gains on the CM3 and significantly outperforms a more complex state-ofthe-art predictive method in cross-validation experiments. Finally, we demonstrate our method on additional platforms by constructing two more optimization levels that find even more significant speed-ups on the ARM Cortex-A8 and 8-bit AVR processors.

Using Agreement Statements to Identify Majority Opinion in UKHL Case Law

International Conference on Legal Knowledge and Information Systems, Dec 31, 2018

This paper is concerned with the task of finding majority opinion (MO) in UK House of Lords (UKHL... more This paper is concerned with the task of finding majority opinion (MO) in UK House of Lords (UKHL) case law by analysing agreement statements (AS) that explicitly express the appointed judges' acceptance of each other's reasoning. We introduce a corpus of 300 UKHL cases in which the relevant AS and MO have been annotated by three legal experts; and we introduce an AI system that automatically identifies this AS and MO with a performance comparable to humans.

Interactive Model Refinement in Relational Domains with Inductive Logic Programming

Figure 1: Overall flow of the presented interactive ILP system. Relational data is ingested and e... more Figure 1: Overall flow of the presented interactive ILP system. Relational data is ingested and encoded as logical facts within a knowledge base (the Background Knowledge). The ILP model uses this knowledge, along with separate files that define the class labels of each sample, to induce an initial rule-set (hypothesis). This hypothesis is subsequently presented to the user for inspection. The user can then iteratively feed back to the model via specialised ILP mechanisms to guide it towards a more desirable hypothesis.

Summarisation with Majority Opinion

Frontiers in artificial intelligence and applications, Dec 1, 2020

This paper introduces a method called SUmmarisation with Majority Opinion (SUMO) that integrates ... more This paper introduces a method called SUmmarisation with Majority Opinion (SUMO) that integrates and extends two prior approaches for abstractively and extractively summarising UK House of Lords cases. We show how combining two previously distinct lines of work allows us to better address the challenges resulting from this court's unusual tradition of publishing the opinions of multiple judges with no formal statement of the reasoning (if any) agreed by a majority. We do this by applying natural language processing and machine learning, Conditional Random Fields (CRFs), to a data set we created by fusing together expert-annotated sentence labels from the HOLJ corpus of rhetorical role summary relevance with the ASMO corpus of agreement statement and majority opinion. By using CRFs and a bespoke summary generator on our enriched data set, we show a significant quantitative F1-score improvement in rhetorical role and relevance classification of 10-15% over the state-of-the-art SUM system; and we show a significant qualitative improvement in the quality of our summaries, which closely resemble goldstandard multi-judge abstracts according to a proof-of-principle user study.

Inferring process models from temporal data with abduction and induction

This paper shows how automated abduction and induction can be used to infer logical process model... more This paper shows how automated abduction and induction can be used to infer logical process models from temporal observations of states and actions. The proposed method employs a non-monotonic learning system called eXtended Hybrid Abductive Inductive Learning (XHAIL) to learn domain axioms in a temporal logic programming formalism known as the Event Calculus (EC). The key benefit of this logical learning method is its ability to utilise background knowledge and to return human understandable hypotheses. The approach is illustrated on a simplified biological process modelling task.

From Case Law to Ratio Decidendi

Lecture Notes in Computer Science, 2018

This paper is concerned with the task of automatically identifying legally binding principles, kn... more This paper is concerned with the task of automatically identifying legally binding principles, known as ratio decidendi or just ratio, from transcripts of court judgements, also called case law or just cases. After briefly reviewing the relevant definitions and previous work in the area, we present a novel system for automatically extracting ratio from cases using a combination of natural language processing and machine learning. Our approach is based on the hypothesis that the ratio of a given case can be reliably obtained by identifying statements of legal principles in paragraphs that are cited by subsequent cases. Our method differs from related recent work by extracting principles from the text of the cited paragraphs (in the given case) as opposed to the text of the citing paragraphs (in a subsequent case). We conduct our own independent small-scale annotation study which reveals that this seemingly subtle shift of focus substantially increases reliability of finding the ratio. Then, by building on previous work in the automatic detection of legal principles and cross citations, we present a fully automated system that successfully identifies the ratio (in our study) with an accuracy of 72%.

Learning and Revising Dynamic Temporal Theories in the Full Discrete Event Calculus

Springer eBooks, 2022

This paper shows how a nonmonotonic ILP system XHAIL can perform general-purpose learning and rev... more This paper shows how a nonmonotonic ILP system XHAIL can perform general-purpose learning and revision of temporal theories in a full-fledged Discrete Event Calculus (DEC) framework with several features (now introduced into ILP for the first time) for dynamically releasing fluents from the commonsense law of inertia and subjecting them to mathematically-defined gradual change trajectories. First, we review key aspects in the development of the DEC and prior attempts aimed at learning within simple fragments of it. Then, we introduce a new formulation of DEC, called the eXploratory Event Calculus (XEC), which (a) results in significantly reduced grounding size and execution times with respect to state-of-the-art translations of DEC from circumscriptive logic into Answer Set Programming (ASP), and which (b) provides a unifying framework that supports different policies used (explicitly or implicitly) in prior work to resolve conflicts between concurrent events competing to determine the truth value and/or release status of fluents under a circumscriptive or answer set semantics. Finally, we give the first known demonstration of learning and revision of theories in the full DEC by using XHAIL and XEC in a proof-of-principle example showing how such theories can be revised under different conflict resolution policies.

CISIS IIBM Welcome Message

Advanced information processing technologies have the potential to significantly accelerate resea... more Advanced information processing technologies have the potential to significantly accelerate research in molecular biology and medicine. In particular, techniques from Artificial Intelligence, Machine Learning, and Data Mining can assist researchers in the discovery of new knowledge needed to deliver improved healthcare and personalized medicine. This workshop aims to attract state-of-the-art solutions and novel attempts in this direction.

Representation, Simulation, and Hypothesis Generation in Graph and Logical Models of Biological Networks

Methods in molecular biology, 2011

This chapter presents a discussion of metabolic modeling from graph theory and logical modeling p... more This chapter presents a discussion of metabolic modeling from graph theory and logical modeling perspectives. These perspectives are closely related and focus on the coarse structure of metabolism, rather than the finer details of system behavior. The models have been used as background knowledge for hypothesis generation by Robot Scientists using yeast as a model eukaryote, where experimentation and machine learning are used to identify additional knowledge to improve the metabolic model. The logical modeling concept is being adapted to cell signaling and transduction biological networks.

Nonmonotonic Learning in Large Biological Networks

Springer eBooks, 2015

This paper introduces a new open-source implementation of a nonmonotonic learning method called X... more This paper introduces a new open-source implementation of a nonmonotonic learning method called XHAIL and shows how it can be used for abductive and inductive inference on metabolic networks that are many times larger than could be handled by the preceding prototype. We summarise several implementation improvements that increase its efficiency and we introduce an extended form of language bias that further increases its usability. We investigate the system's scalability in a case study involving real data previously collected by a Robot Scientist and show how it led to the discovery of an error in a whole-organism model of yeast metabolism.

Logic-Based Steady-State Analysis and Revision of Metabolic Networks with Inhibition

This paper presents a qualitative logic-based method for the steady-state analysis and revision o... more This paper presents a qualitative logic-based method for the steady-state analysis and revision of metabolic networks with inhibition. The approach is able to automatically revise an initial metabolic model -through the addition and removal of whole reactions or individual substrates, products and inhibitors -in order to ensure the existence of a steady-state behaviour consistent with a set of experimental observations. We show how this can be done in a nonmonotonic logic programming setting and discuss the challenges that arise when metabolic cycles or mutual inhibitions occur in the underlying network.

A Neural Network Approach for First-Order Abductive Inference

This paper presents a neural network approach for first-order abductive inference by generalising... more This paper presents a neural network approach for first-order abductive inference by generalising an existing method from propositional logic to the first-order case. We show how the original propositional method can be extended to enable the grounding of a first-order abductive problem; and we also show how it can be modified to allow the prioritised computation of minimal solutions. We illustrate the approach on a well-known abductive problem and explain how it can be used to perform first-order conditional query answering.

Automatic Revision of Metabolic Networks through Logical Analysis of Experimental Data

Springer eBooks, 2010

This paper presents a nonmonotonic ILP approach for the automatic revision of metabolic networks ... more This paper presents a nonmonotonic ILP approach for the automatic revision of metabolic networks through the logical analysis of experimental data. The method extends previous work in two respects: by suggesting revisions that involve both the addition and removal of information; and by suggesting revisions that involve combinations of gene functions, enzyme inhibitions, and metabolic reactions. Our proposal is based on a new declarative model of metabolism expressed in a nonmonotonic logic programming formalism. With respect to this model, a mixture of abductive and inductive inference is used to compute a set of minimal revisions needed to make a given network consistent with some observed data. In this way, we describe how a reasoning system called XHAIL was able to correctly revise a state-of-the-art metabolic pathway in the light of real-world experimental data acquired by an autonomous laboratory platform called the Robot Scientist.

AstronomicAL: an interactive dashboard for visualisation, integration and classification of data with Active Learning

Journal of open source software, Sep 3, 2021

AstronomicAL is a human-in-the-loop interactive labelling and training dashboard that allows user... more AstronomicAL is a human-in-the-loop interactive labelling and training dashboard that allows users to create reliable datasets and robust classifiers using active learning. This technique prioritises data that offer high information gain, leading to improved performance using substantially less data. The system allows users to visualise and integrate data from different sources and deal with incorrect or missing labels and imbalanced class sizes. AstronomicAL enables experts to visualise domain-specific plots and key information relating both to broader context and details of a point of interest drawn from a variety of data sources, ensuring reliable labels. In addition, AstronomicAL provides functionality to explore all aspects of the training process, including custom models and query strategies. This makes the software a tool for experimenting with both domain-specific classifications and more general-purpose machine learning strategies. We illustrate using the system with an astronomical dataset due to the field's immediate need; however, AstronomicAL has been designed for datasets from any discipline. Finally, by exporting a simple configuration file, entire layouts, models, and assigned labels can be shared with the community. This allows for complete transparency and ensures that the process of reproducing results is effortless.

Towards a Logical Reconstruction of CF-Induction

Lecture Notes in Computer Science, 2008

CF-induction is a sound and complete hypothesis finding procedure for full clausal logic which us... more CF-induction is a sound and complete hypothesis finding procedure for full clausal logic which uses the principle of inverse entailment to compute a hypothesis that logically explains a set of examples with respect to a prior background theory. Currently, CF-induction computes hypotheses by applying combinations of several complex generalisation operators to an intermediate theory called a bridge formula. In this paper we propose an alternative approach whereby hypotheses are derived from a bridge formula using a single deductive operator and a single inductive operator. We show that our simplified procedure preserves the soundness and completeness of CF-induction.

Analyzing Pathways Using ASP-Based Approaches

Lecture Notes in Computer Science, 2012

This paper contributes to a line of research which aims to combine numerical information with log... more This paper contributes to a line of research which aims to combine numerical information with logical inference in order to find the most likely states of a biological system under various (actual or hypothetical) constraints. To this end, we build upon a state-of-the-art approach that employs weighted Boolean constraints to represent and reason about biochemical reaction networks. Our first contribution is to show how this existing method fails to deal satisfactorily with networks that contain cycles. Our second contribution is to define a new method which correctly handles such cases by exploiting the formalism of Answer Set Programming (ASP). We demonstrate the significance of our results on two case-studies previously studied in the literature.

Towards a Rational Approach for the Logical Modelling of Inhibition in Metabolic Networks

This paper makes two contributions towards the logical modelling of inhibition in metabolic netwo... more This paper makes two contributions towards the logical modelling of inhibition in metabolic networks. First it exposes the logical inconsistency of an existing state-of-the-art metabolic theory. Then it proposes a new model from which the required hypotheses are consistently inferred. Underlying this work is use of nonmonotonic logic programs to represent and reason about competing inhibitory effects on the net concentrations of metabolites in enzyme-catalysed biochemical networks.

The Need for Ancestor Resolution When Answering Queries in Horn Clause Logic

Springer eBooks, 2005

Prolog Technology Theorem Proving. (PTTP) [5] is a well known extension of Prolog for answering q... more Prolog Technology Theorem Proving. (PTTP) [5] is a well known extension of Prolog for answering queries in first-order logic. PTTP is based on the idea that 'Prolog can be viewed as an "almost complete" theorem prover, which has to be extended by only a few ingredients in order to handle the non-Horn case' . As explained in [5], PTTP is an efficient realisation of the Model Elimination (ME) calculus [2] that utilises five extensions of standard Prolog:

Nonmonotonic abductive inductive learning

Journal of Applied Logic, Sep 1, 2009

Inductive Logic Programming (ILP) is concerned with the task of generalising sets of positive and... more Inductive Logic Programming (ILP) is concerned with the task of generalising sets of positive and negative examples with respect to background knowledge expressed as logic programs. Negation as Failure (NAF) is a key feature of logic programming which provides a means for nonmonotonic commonsense reasoning under incomplete information. But, so far, most ILP research has been aimed at Horn programs which exclude NAF, and has failed to exploit the full potential of normal programs that allow NAF. By contrast, Abductive Logic Programming (ALP), a related task concerned with explaining observations with respect to a prior theory, has been well studied and applied in the context of normal logic programs. This paper shows how ALP can be used to provide a semantics and proof procedure for nonmonotonic ILP that utilises practical methods of language and search bias to reduce the search space. This is done by lifting an existing method called Hybrid Abductive Inductive Learning (HAIL) from Horn clauses to normal logic programs. To demonstrate its potential benefits, the resulting system, called XHAIL, is applied to a process modelling case study involving a nonmonotonic temporal Event Calculus (EC).

Automatically Tuning the GCC Compiler to Optimize the Performance of Applications Running on the ARM Cortex-M3

arXiv (Cornell University), Feb 23, 2017

This paper introduces a novel method for automatically tuning the selection of compiler flags to ... more This paper introduces a novel method for automatically tuning the selection of compiler flags to optimize the performance of software intended to run on embedded hardware platforms. We begin by developing our approach on code compiled by the GNU C Compiler (GCC) for the ARM Cortex-M3 (CM3) processor; and we show how our method outperforms the industry standard -O3 optimization level across a diverse embedded benchmark suite. First we quantify the potential gains by using existing iterative compilation approaches that time-intensively search for optimal configurations for each benchmark. Then we adapt iterative compilation to output a single configuration that optimizes performance across the entire benchmark suite. Although this is a time-consuming process, our approach constructs an optimized variation of -O3, which we call -Ocm3, that realizes nearly two thirds of known available gains on the CM3 and significantly outperforms a more complex state-ofthe-art predictive method in cross-validation experiments. Finally, we demonstrate our method on additional platforms by constructing two more optimization levels that find even more significant speed-ups on the ARM Cortex-A8 and 8-bit AVR processors.

Automatically Tuning the GCC Compiler to Optimize the Performance of Applications Running on Embedded Systems

arXiv (Cornell University), Feb 23, 2017

This paper introduces a novel method for automatically tuning the selection of compiler flags to ... more This paper introduces a novel method for automatically tuning the selection of compiler flags to optimize the performance of software intended to run on embedded hardware platforms. We begin by developing our approach on code compiled by the GNU C Compiler (GCC) for the ARM Cortex-M3 (CM3) processor; and we show how our method outperforms the industry standard -O3 optimization level across a diverse embedded benchmark suite. First we quantify the potential gains by using existing iterative compilation approaches that time-intensively search for optimal configurations for each benchmark. Then we adapt iterative compilation to output a single configuration that optimizes performance across the entire benchmark suite. Although this is a time-consuming process, our approach constructs an optimized variation of -O3, which we call -Ocm3, that realizes nearly two thirds of known available gains on the CM3 and significantly outperforms a more complex state-ofthe-art predictive method in cross-validation experiments. Finally, we demonstrate our method on additional platforms by constructing two more optimization levels that find even more significant speed-ups on the ARM Cortex-A8 and 8-bit AVR processors.

Using Agreement Statements to Identify Majority Opinion in UKHL Case Law

International Conference on Legal Knowledge and Information Systems, Dec 31, 2018

This paper is concerned with the task of finding majority opinion (MO) in UK House of Lords (UKHL... more This paper is concerned with the task of finding majority opinion (MO) in UK House of Lords (UKHL) case law by analysing agreement statements (AS) that explicitly express the appointed judges' acceptance of each other's reasoning. We introduce a corpus of 300 UKHL cases in which the relevant AS and MO have been annotated by three legal experts; and we introduce an AI system that automatically identifies this AS and MO with a performance comparable to humans.

Interactive Model Refinement in Relational Domains with Inductive Logic Programming

Figure 1: Overall flow of the presented interactive ILP system. Relational data is ingested and e... more Figure 1: Overall flow of the presented interactive ILP system. Relational data is ingested and encoded as logical facts within a knowledge base (the Background Knowledge). The ILP model uses this knowledge, along with separate files that define the class labels of each sample, to induce an initial rule-set (hypothesis). This hypothesis is subsequently presented to the user for inspection. The user can then iteratively feed back to the model via specialised ILP mechanisms to guide it towards a more desirable hypothesis.

Summarisation with Majority Opinion

Frontiers in artificial intelligence and applications, Dec 1, 2020

This paper introduces a method called SUmmarisation with Majority Opinion (SUMO) that integrates ... more This paper introduces a method called SUmmarisation with Majority Opinion (SUMO) that integrates and extends two prior approaches for abstractively and extractively summarising UK House of Lords cases. We show how combining two previously distinct lines of work allows us to better address the challenges resulting from this court's unusual tradition of publishing the opinions of multiple judges with no formal statement of the reasoning (if any) agreed by a majority. We do this by applying natural language processing and machine learning, Conditional Random Fields (CRFs), to a data set we created by fusing together expert-annotated sentence labels from the HOLJ corpus of rhetorical role summary relevance with the ASMO corpus of agreement statement and majority opinion. By using CRFs and a bespoke summary generator on our enriched data set, we show a significant quantitative F1-score improvement in rhetorical role and relevance classification of 10-15% over the state-of-the-art SUM system; and we show a significant qualitative improvement in the quality of our summaries, which closely resemble goldstandard multi-judge abstracts according to a proof-of-principle user study.

Inferring process models from temporal data with abduction and induction

This paper shows how automated abduction and induction can be used to infer logical process model... more This paper shows how automated abduction and induction can be used to infer logical process models from temporal observations of states and actions. The proposed method employs a non-monotonic learning system called eXtended Hybrid Abductive Inductive Learning (XHAIL) to learn domain axioms in a temporal logic programming formalism known as the Event Calculus (EC). The key benefit of this logical learning method is its ability to utilise background knowledge and to return human understandable hypotheses. The approach is illustrated on a simplified biological process modelling task.

From Case Law to Ratio Decidendi

Lecture Notes in Computer Science, 2018

This paper is concerned with the task of automatically identifying legally binding principles, kn... more This paper is concerned with the task of automatically identifying legally binding principles, known as ratio decidendi or just ratio, from transcripts of court judgements, also called case law or just cases. After briefly reviewing the relevant definitions and previous work in the area, we present a novel system for automatically extracting ratio from cases using a combination of natural language processing and machine learning. Our approach is based on the hypothesis that the ratio of a given case can be reliably obtained by identifying statements of legal principles in paragraphs that are cited by subsequent cases. Our method differs from related recent work by extracting principles from the text of the cited paragraphs (in the given case) as opposed to the text of the citing paragraphs (in a subsequent case). We conduct our own independent small-scale annotation study which reveals that this seemingly subtle shift of focus substantially increases reliability of finding the ratio. Then, by building on previous work in the automatic detection of legal principles and cross citations, we present a fully automated system that successfully identifies the ratio (in our study) with an accuracy of 72%.