Federico Cerutti's research while affiliated with Cardiff University and other places

Publications (109)

Article
In this paper we illustrate how novel AI methods can improve the performance of intelligence analysts. These analysts aim to make sense of — often conflicting or incomplete — information, weighing up competing hypotheses which serve to explain an observed situation. Analysts have access to numerous visual analytic tools which support the temporal a...
Article
The majority of websites incorporate JavaScript for client-side execution in a supposedly protected environment. Unfortunately, JavaScript has also proven to be a critical attack vector for both independent and state-sponsored groups of hackers. On the one hand, defenders need to analyze scripts to ensure that no threat is delivered and to respond...
Chapter
Full-text available
We propose a generic notion of consistency in an abstract labelling setting, based on two relations: one of intolerance between the labelled elements and one of incompatibility between the labels assigned to them, thus allowing a spectrum of consistency requirements depending on the actual choice of these relations. As a first application to formal...
Preprint
The sixth assessment of the international panel on climate change (IPCC) states that "cumulative net CO2 emissions over the last decade (2010-2019) are about the same size as the 11 remaining carbon budget likely to limit warming to 1.5C (medium confidence)." Such reports directly feed the public discourse, but nuances such as the degree of belief...
Preprint
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In second-order uncertain Bayesian networks, the conditional probabilities are only known within distributions, i.e., probabilities over probabilities. The delta-method has been applied to extend exact first-order inference methods to propagate both means and variances through sum-product networks derived from Bayesian networks, thereby characteriz...
Preprint
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When the historical data are limited, the conditional probabilities associated with the nodes of Bayesian networks are uncertain and can be empirically estimated. Second order estimation methods provide a framework for both estimating the probabilities and quantifying the uncertainty in these estimates. We refer to these cases as uncer tain or seco...
Conference Paper
When collaborating with an artificial intelligence (AI) system, we need to assess when to trust its recommendations. Suppose we mistakenly trust it in regions where it is likely to err. In that case, catastrophic failures may occur, hence the need for Bayesian approaches for reasoning and learning to determine the confidence (or epistemic uncertain...
Chapter
This paper introduces argumentative-generative models for statistical learning—i.e., generative statistical models seen from a Bayesian argumentation perspective—and shows how they support trustworthy artificial intelligence (AI). Generative Bayesian approaches are already very promising for achieving robustness against adversarial attacks, a funda...
Conference Paper
Emergent communication can lead to more efficient problem-solving heuristics and more domain specificity. It can perform better than a handcrafted communication protocol, potentially directing autonomous agents towards unforeseen yet effective solutions. Previous research has investigated a social deduction game, called Werewolf, where two groups o...
Article
Full-text available
When collaborating with an AI system, we need to assess when to trust its recommendations. If we mistakenly trust it in regions where it is likely to err, catastrophic failures may occur, hence the need for Bayesian approaches for probabilistic reasoning in order to determine the confidence (or epistemic uncertainty) in the probabilities in light o...
Chapter
The paper introduces a general model for the study of decomposability in abstract argumentation, i.e. the possibility of determining the semantics outcome based on local evaluations in subframeworks. As such, the paper extends a previous work by generalizing over the kind of information locally exploited. While not concerned with specific semantics...
Preprint
Full-text available
In this paper, we present an approach to Complex Event Processing (CEP) that is based on DeepProbLog. This approach has the following objectives: (i) allowing the use of subsymbolic data as an input, (ii) retaining the flexibility and modularity on the definitions of complex event rules, (iii) allowing the system to be trained in an end-to-end mann...
Preprint
We present Fudge, an abstract argumentation solver that tightly integrates satisfiability solving technology to solve a series of abstract argumentation problems. While most of the encodings used by Fudge derive from standard translation approaches, Fudge makes use of completely novel encodings to solve the skeptical reasoning problem wrt. preferre...
Chapter
In this paper we introduce \(\textsf {AASExts}\), an algorithm for computing semi–stable extensions. We improve techniques developed for other semantics, notably preferred semantics, as well as leverage recent advances in All-SAT community. We prove our proposed algorithm is sound and complete, we describe the experiments to select the most appropr...
Conference Paper
We address the problem of deciding skeptical acceptance wrt. preferred semantics of an argument in abstract argumentation frameworks, i. e., the problem of deciding whether an argument is contained in all maximally admissible sets, a.k.a. preferred extensions. State-of-the-art algorithms solve this problem with iterative calls to an external SAT- s...
Preprint
When collaborating with an AI system, we need to assess when to trust its recommendations. If we mistakenly trust it in regions where it is likely to err, catastrophic failures may occur, hence the need for Bayesian approaches for probabilistic reasoning in order to determine the confidence (or epistemic uncertainty) in the probabilities in light o...
Article
Full-text available
In this paper we ask whether approximation for abstract argumentation is useful in practice, and in particular whether reasoning with grounded semantics – which has polynomial runtime – is already an approximation approach sufficient for several practical purposes. While it is clear from theoretical results that reasoning with grounded semantics is...
Preprint
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We present an experimentation platform for coalition situational understanding research that highlights capabilities in explainable artificial intelligence/machine learning (AI/ML) and integration of symbolic and subsymbolic AI/ML approaches for event processing. The Situational Understanding Explorer (SUE) platform is designed to be lightweight, t...
Preprint
Future coalition operations can be substantially augmented through agile teaming between human and machine agents, but in a coalition context these agents may be unfamiliar to the human users and expected to operate in a broad set of scenarios rather than being narrowly defined for particular purposes. In such a setting it is essential that the hum...
Conference Paper
We investigate the computational problem of determining the set of acceptable arguments in abstract argumentation wrt. credulous and skeptical reasoning under grounded, complete, stable, and preferred semantics. In particular, we investigate the computational complexity of that problem and its verification variant, and develop four SAT-based algori...
Preprint
Training a model to detect patterns of interrelated events that form situations of interest can be a complex problem: such situations tend to be uncommon, and only sparse data is available. We propose a hybrid neuro-symbolic architecture based on Event Calculus that can perform Complex Event Processing (CEP). It leverages both a neural network to i...
Article
Full-text available
Artificial intelligence (AI) systems hold great promise as decision-support tools, but we must be able to identify and understand their inevitable mistakes if they are to fulfill this potential. This is particularly true in domains where the decisions are high-stakes, such as law, medicine, and the military. In this Perspective, we describe the par...
Preprint
Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify classification uncertainty directly by training the model to output high uncertainty for the data samples close to class boundaries or from the outside of the training distribution. These appro...
Article
Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify classification uncertainty directly by training the model to output high uncertainty for the data samples close to class boundaries or from the outside of the training distribution. These appro...
Preprint
Full-text available
Automated negotiation has been used in a variety of distributed settings, such as privacy in the Internet of Things (IoT) devices and power distribution in Smart Grids. The most common protocol under which these agents negotiate is the Alternating Offers Protocol (AOP). Under this protocol, agents cannot express any additional information to each o...
Conference Paper
Full-text available
0000−0002−5485−6571] , Angelika Kimmig 1[0000−0002−6742−4057] , Alun Preece 1[0000−0003−0349−9057] , and Federico Cerutti 2 1[0000−0003−0755−0358] Abstract Automated negotiation has been used in a variety of distributed settings, such as privacy in the Internet of Things (IoT) devices and power distribution in Smart Grids. The most common protocol...
Preprint
Full-text available
Automated negotiation can be an efficient method for resolving conflict and redistributing resources in a coalition setting. Automated negotiation has already seen increased usage in fields such as e-commerce and power distribution in smart girds, and recent advancements in opponent modelling have proven to deliver better outcomes. However, signifi...
Conference Paper
Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify classification uncertainty directly by training the model to output high uncertainty for the data samples close to class boundaries or from the outside of the training distribution. These appro...
Chapter
Automated negotiation has been used in a variety of distributed settings, such as privacy in the Internet of Things (IoT) devices and power distribution in Smart Grids. The most common protocol under which these agents negotiate is the Alternating Offers Protocol (AOP). Under this protocol, agents cannot express any additional information to each o...
Preprint
Central to the concept of multi-domain operations (MDO) is the utilization of an intelligence, surveillance, and reconnaissance (ISR) network consisting of overlapping systems of remote and autonomous sensors, and human intelligence, distributed among multiple partners. Realising this concept requires advancement in both artificial intelligence (AI...
Article
In this paper we illustrate the design choices that led to the development of ArgSemSAT, the winner of the preferred semantics track at the 2017 International Competition on Computational Models of Arguments (ICCMA 2017), a biennial contest on problems associated to the Dung's model of abstract argumentation frameworks, widely recognised as a funda...
Article
We enable aProbLog—a probabilistic logical programming approach—to reason in presence of uncertain probabilities represented as Beta-distributed random variables. We achieve the same performance of state-of-the-art algorithms for highly specified and engineered domains, while simultaneously we maintain the flexibility offered by aProbLog in handlin...
Article
We propose a general scheme for adding probabilistic reasoning capabilities to a wide variety of knowledge representation formalisms and we study its properties. Syntactically, we consider adding probabilities to the formulas of a given base logic. Semantically, we define a probability distribution over the subsets of a knowledge base by taking the...
Article
In this paper, we describe how predictive models can be positively exploited in abstract argumentation. In particular, we present two main sets of results. On one side, we show that predictive models are effective for performing algorithm selection in order to determine which approach is better to enumerate the preferred extensions of a given argum...
Conference Paper
p>In this paper we provide a first analysis of the research questions that arise when dealing with the problem of communicating pieces of formal argumentation through natural language interfaces. It is a generally held opinion that formal models of argumentation naturally capture human argument, and some preliminary studies have focused on justifyi...
Article
Full-text available
The inability of current machines to expose biases induced by programmers and data scientists is leading towards the creation of a new religion, where machines are mystic oracles whose pronouncements have to be believed, and computer users are their servants. This has to change. In this paper we discuss the issues that can raise from biases introdu...
Preprint
This paper argues the need for research to realize uncertainty-aware artificial intelligence and machine learning (AI\&ML) systems for decision support by describing a number of motivating scenarios. Furthermore, the paper defines uncertainty-awareness and lays out the challenges along with surveying some promising research directions. A theoretica...
Preprint
We enable aProbLog---a probabilistic logical programming approach---to reason in presence of uncertain probabilities represented as Beta-distributed random variables. We achieve the same performance of state-of-the-art algorithms for highly specified and engineered domains, while simultaneously we maintain the flexibility offered by aProbLog in han...
Conference Paper
In this paper we propose a computational methodology for assessing the impact of trust associated to sources of information in situational understanding activities—i.e. relating relevant information and form logical conclusions, as well as identifying gaps in information in order to answer a given query. Often trust in the source of information ser...
Conference Paper
p>We introduce CISpaces.org, a tool to support situational understanding in intelligence analysis that complements but not replaces human expertise. The system combines natural language processing, argumentation-based reasoning, and natural language generation to produce intelligence reports from social media data, and to record the process of form...
Conference Paper
p>We demonstrate CISpaces.org, a tool to support situational understanding in intelligence analysis that complements but not replaces human expertise, for the first time applied to a judicial context. The system combines argumentationbased reasoning and natural language generation to support the creation of analysis and summary reports, and to reco...
Article
2018 The authors and IOS Press. This paper briefly describes AIF-EL, an OWL2-EL compliant ontology for the Argument Interchange Format.
Conference Paper
p>This paper briefly describes AIF-EL, an OWL2-EL compliant ontology for the Argument Interchange Format.</p
Article
In this paper we consider the impact of configuration of abstract argumentation reasoners both when using a single solver and choosing combinations of framework representation–solver options; and also when composing portfolios of algorithms. To exemplify the impact of the framework–solver configuration we consider one of the most configurable solve...
Conference Paper
In the light of the increasing interest in efficient algorithms for solving abstract argumentation problems and the pervasive availability of multicore machines, a natural research issue is to combine existing argumentation solvers into parallel portfolios. In this work, we introduce six methodologies for the automatic configuration of parallel por...
Article
In this paper we provide a first analysis of the research questions that arise when dealing with the problem of communicating pieces of formal argumentation through natural language interfaces. It is a generally held opinion that formal models of argumentation naturally capture human argument, and some preliminary studies have focused on justifying...
Article
Full-text available
Optimization - minimization or maximization - in the lattice of subsets is a frequent operation in Artificial Intelligence tasks. Examples are subset-minimal model-based diagnosis, nonmonotonic reasoning by means of circumscription, or preferred extensions in abstract argumentation. Finding the optimum among many admissible solutions is often harde...
Article
Dung's argumentation frameworks are adopted in a variety of applications, from argument-mining, to intelligence analysis and legal reasoning. Despite this broad spectrum of already existing applications, the mostly adopted solver-in virtue of its simplicity-is far from being comparable to the current state-of-The-Art solvers. On the other hand, mos...
Conference Paper
Principal Agent Theory (PAT) seeks to identify the incentives and sanctions that a consumer should apply when entering into a contract with a provider in order to maximise their own utility. However, identifying suitable contracts—maximising utility while minimising regret— is difficult, particularly when little information is available about provi...
Article
Desire conflicts arise in several real-world contexts. In this paper, we propose a mixed deliberation dialogue for reconciliation. A mixed deliberation dialogue is defined as a combination of forward and backward deliberation dialogues with respective goals which are subordinate and superordinate desires of a given desire. This research and the int...
Article
We review the First International Competition on Computational Models of Argumentation (ICCMA'15). The competition evaluated submitted solvers performance on four different computational tasks related to solving abstract argumentation frameworks. Each task evaluated solvers in ways that pushed the edge of existing performance by introducing new cha...
Article
Argumentation Frameworks (AFs) provide a fruitful basis for exploring issues of defeasible reasoning. Their power largely derives from the abstract nature of the arguments within the framework, where arguments are atomic nodes in an undifferentiated relation of attack. This abstraction conceals different senses of argument, namely a single-step rea...
Conference Paper
The aim of intelligence analysis is to make sense of information that is often conflicting or incomplete, and to weigh competing hypotheses that may explain a situation. This imposes a high cognitive load on analysts, and there are few automated tools to aid them in their task. In this paper, we present an agent-based tool to help analysts in acqui...
Article
The aim of intelligence analysis is to make sense of information that is often conflicting or incomplete, and to weigh competing hypotheses that may explain a situation. This imposes a high cognitive load on analysts, and there are few automated tools to aid them in their task. In this paper, we present an agent-based tool to help analysts in acqui...
Article
Principal Agent Theory (PAT) seeks to identify incentives and sanctions that a consumer should offer a producer as part of a contract in order to maximise the former's utility. However, identifying optimal contracts in large systems is difficult, particularly when little information is available about producer competencies. In this work we propose...
Conference Paper
Desire conflicts arise in several real-world contexts. In this paper we propose a mixed deliberation dialogue for reconciliation. A mixed deliberation dialogue is defined as a combination of forward and backward deliberation dialogues whose goals are subordinate and superordinate desires of a given desire, respectively. This research and the introd...