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Jeroen Keppens

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6 papers

2 author rows

EUMAS Conference 2015 Conference Paper

Agent Based Simulation to Evaluate Adaptive Caching in Distributed Databases

Santhilata Kuppili Venkata
Jeroen Keppens
Katarzyna Musial

Abstract Caching frequently used data is a common practice to improve query performance in database systems. But traditional algorithms used for cache management prove to be insufficient in distributed environment where groups of users require similar or related data from multiple databases. Repeated data transfers can become a bottleneck leading to long query response time and high resource utilization. Our work focuses on adaptive algorithms to decide on optimal grain of data to be cached and cache refreshment techniques to reduce data transfers. In this paper, we present agent based simulation to investigate and in consequence improve cache management in the distributed database environment. Dynamic grain size and decisions on cache refreshment are made as a result of coordination and interaction between agents. Initial results show better response time and higher data availability compared to traditional caching techniques.

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AILAW Journal 2015 Journal Article

Establishing norms with metanorms in distributed computational systems

Samhar Mahmoud
Nathan Griffiths
Jeroen Keppens
Adel Taweel
Trevor J. M. Bench-Capon
Michael Luck

Abstract Norms provide a valuable mechanism for establishing coherent cooperative behaviour in decentralised systems in which there is no central authority. One of the most influential formulations of norm emergence was proposed by Axelrod (Am Political Sci Rev 80(4): 1095–1111, 1986 ). This paper provides an empirical analysis of aspects of Axelrod’s approach, by exploring some of the key assumptions made in previous evaluations of the model. We explore the dynamics of norm emergence and the occurrence of norm collapse when applying the model over extended durations. It is this phenomenon of norm collapse that can motivate the emergence of a central authority to enforce laws and so preserve the norms, rather than relying on individuals to punish defection. Our findings identify characteristics that significantly influence norm establishment using Axelrod’s formulation, but are likely to be of importance for norm establishment more generally. Moreover, Axelrod’s model suffers from significant limitations in assuming that private strategies of individuals are available to others, and that agents are omniscient in being aware of all norm violations and punishments. Because this is an unreasonable expectation, the approach does not lend itself to modelling real-world systems such as online networks or electronic markets. In response, the paper proposes alternatives to Axelrod’s model, by replacing the evolutionary approach, enabling agents to learn, and by restricting the metapunishment of agents to cases where the original defection is observed, in order to be able to apply the model to real-world domains. This work can also help explain the formation of a “social contract” to legitimate enforcement by a central authority.

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AILAW Journal 2014 Journal Article

On modelling non-probabilistic uncertainty in the likelihood ratio approach to evidential reasoning

Jeroen Keppens

Abstract When the likelihood ratio approach is employed for evidential reasoning in law, it is often necessary to employ subjective probabilities, which are probabilities derived from the opinions and judgement of a human (expert). At least three concerns arise from the use of subjective probabilities in legal applications. Firstly, human beliefs concerning probabilities can be vague, ambiguous and inaccurate. Secondly, the impact of this vagueness, ambiguity and inaccuracy on the outcome of a probabilistic analysis is not necessarily fully understood. Thirdly, the provenance of subjective probabilities and the associated potential sources of vagueness, ambiguity and inaccuracy tend to be poorly understood, making it difficult for the outcome of probabilistic reasoning to be explained and validated, which is crucial in legal applications. The former two concerns have been addressed by a wide body of research in AI. The latter, however, has received little attention. This paper presents a novel approach to employ argumentation to reason about probability distributions in probabilistic models. It introduces a range of argumentation schemes and corresponding sets of critical questions for the construction and validation of argument models that define sets of probability distributions. By means of an extended example, the paper demonstrates how the approach, argumentation schemes and critical questions can be employed for the development of models and their validation in legal applications of the likelihood ratio approach to evidential reasoning.

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AILAW Journal 2012 Journal Article

Argument diagram extraction from evidential Bayesian networks

Jeroen Keppens

Abstract Bayesian networks (BN) and argumentation diagrams (AD) are two predominant approaches to legal evidential reasoning, that are often treated as alternatives to one another. This paper argues that they are, instead, complimentary and proposes the beginnings of a method to employ them in such a manner. The Bayesian approach tends to be used as a means to analyse the findings of forensic scientists. As such, it constitutes a means to perform evidential reasoning. The design of Bayesian networks that accurately and comprehensively represent the relationships between investigative hypotheses and evidence remains difficult and sometimes contentious, however. Argumentation diagrams are representations of reasoning, and are used as a means to scrutinise reasoning (among other applications). In evidential reasoning, they tend to be used to represent and scrutinise the way humans reason about evidence. This paper examines how argumentation diagrams can be used to scrutinise Bayesian evidential reasoning by developing a method to extract argument diagrams from BN.

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ECAI Conference 2012 Conference Paper

Efficient Norm Emergence through Experiential Dynamic Punishment

Samhar Mahmoud
Nathan Griffiths
Jeroen Keppens
Michael Luck

Peer punishment has been an effective means to ensure that norms are complied with in a population of self-interested agents. However, current approaches to establishing norms have only considered static punishments, which do not vary with the magnitude or frequency of norm violation. Such static punishments are difficult to apply because it is difficult to identify an appropriate fixed penalty: one that is not too weak to disincentivise norm violations and not too strong to lead to significant deleterious effects on the system as a whole (such as those incurred by losing the benefits of a member of the population). This paper addresses this concern by developing an adaptive punishment technique that tailors penalty to norm violation. An experimental evaluation of the approach demonstrates its value compared to static punishment. In particular, the results show that our dynamic punishment technique is capable of achieving norm emergence, even when starting with an amount of punishment that is too low to achieve emergence in the traditional static approach.

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KER Journal 2001 Journal Article

On compositional modelling

Jeroen Keppens
Qiang Shen

Many solutions to AI problems require the task to be represented in one of a multitude of rigorous mathematical formalisms. The construction of such mathematical models forms a difficult problem which is often left to the user of the problem-solver. This void between problem-solvers and their problems is studied by the eclectic field of automated modelling. Within this field, compositional modelling, a knowledge-based methodology for system-modelling, has established itself as a leading approach. In general, a compositional modeller organises knowledge in a structure of composable fragments that relate to particular system components or processes. Its embedded inference mechanism chooses the appropriate fragments with respect to a given problem, instantiates and assembles them into a consistent system model. Many different types of compositional modeller exist, however, with significant differences in their knowledge representation and approach to inference. This paper examines compositional modelling. It presents a general framework for building and analysing compositional modellers. Based on this framework, a number of influential compositional modellers are examined and compared. The paper also identifies the strengths and weaknesses of compositional modelling and discusses some typical applications.

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