Representing narrative and testimonial knowledge in sense-making software for crime analysis.

In the AVERs sense-making tool for crime analysis different types of information are represented in different ways. More precisely, narrative knowledge is represented in an explanatory direction and testimonial knowledge in an indicative direction. This paper shows that this distinction agrees with the preference of potential users and reduces the number of interpretation errors made by them

Representing narrative and testimonial knowledge in sense-making software for crime analysis.

Abstract. In the AVERs sense-making tool for crime analysis different types of information are represented in different ways. More precisely, narrative knowledge is represented in an explanatory direction and testimonial knowledge in an indicative direction. This paper shows that this distinction agrees with the preference of potential users and reduces the number of interpretation errors made by them

Progress is not optimization : co-evolutionary robotics in simulation

In this thesis, various aspects of the co-evolutionary projects of Nolfi and Floreano will be investigated. First of all, we will test the extent to which their "Evorobot" software allows one to replicate Nolfi and Floreano's experimental results. We will argue that numerous limitations and difficulties make it impossible for us to obtain complete resemblance. Sec- ondly, we will investigate whether progress throughout generations occurs. Moreover, we will test the progress for its monotonicity, using the isotonic regression analysis, and we will see that in all experiments the progress is significantly nondecreasing. Further, we will argue that evolving the individuals within more complex environments will lead to a larger amount of progress, while the use of environmental change or plasticity results in a smaller amount of progress. Finally, we will investigate the extent to which the co-evolutionary robotics of Nolfi and Floreano, as based on their software, is comparable to natural evolution. We will propose 3 criteria (i.e. the concepts, techniques used, and results), in order to examine the naturalness of their co-evolutionary robotics. We will claim that on the basis of these criteria there is no principled reason to deny the biological plausibility of Nolfi and Floreano's approach, although in practice, their co-evolutionary robotics is still far removed from it. Biological plausibility thus remains a fundamental important challenge for future research on co-evolutionary robotics

Sensemaking software for crime analysis

Criminal investigation is a difficult and laborious process that is prone to error as teams of investigators may be subject to tunnel vision, groupthink, and confirmation bias. As a result, miscarriages of justice may ensue. To overcome these problems, in the Dutch law enforcement organization, crime analysts have been given a more important role. It is now their task to critically evaluate the investigation that is going on. They have to make sense of the vast amount of evidence available in a case by generating plausible scenarios about what might have happened. Subsequently, they have to assess the quality of their scenarios and choose the best alternative. Due to the difficulty of this process, a great need exists for software that supports crime analysts in their task. However, current support tools for crime analysis do not allow analysts to record scenarios and their relation to the evidence and as a result the most important part of the analysis process remains in the analysts' minds. Therefore, they may benefit from so-called sensemaking systems that allow them to make their reasoning process explicit by visualizing scenarios and the reasons why these scenarios are supported by the evidence. Nevertheless, such sensemaking tools for crime analysis are relatively sparse and often do not incorporate a logical model of reasoning with evidence in the context of crime analysis. This thesis aims to fill this gap by proposing sensemaking software that has specifically been designed for crime analysis. Such a tool should be rationally well-founded, natural, useful, usable, and effective. To this aid, a proof-of-concept application called AVERs (Argument Visualization for Evidential Reasoning based on stories) was built that implements a rationally well-founded and natural model of the reasoning that takes place in crime analysis. In this way a standard of rational reasoning is encouraged and errors may be reduced. Using AVERs analysts are able to create visual representations of scenarios and evidential arguments. Scenarios are represented as causal networks of events, while evidential arguments are arguments based on the evidential data in the case. Such arguments are based on argumentation schemes that often come with critical questions. These questions make the analysts more aware of possible sources of doubt and encourage them to critically examine the evidence. Evidential arguments can be used to support or attack scenarios with the available evidence. In this way, this software allows the analysts to reason about scenarios and to critically evaluate them. Moreover, it provides features that can be used to compare alternative scenarios. A series of empirical studies has confirmed that the design and implementation of AVERs fulfills all five criteria to a certain degree. This means that it is useful to crime analysts and satisfies their desires, while it may improve their analysis of the case and the communication of their results to the investigators working on the case, and ensures that rational analyses are performed. Therefore, through this software in the future biases in the crime analysis process may be avoided

AVERs:An argument visualization tool for representing stories about evidence

This paper proposes an architecture for a sense-making system for crime investigation named AVERs (Argument Visualization for Evidential Reasoning based on stories). It is targeted at crime investigators who may use it to explain initially observed facts by drawing links between these facts and hypothesized events, and to connect the thus created stories to evidence through argumentation. AVERs draws on a combination of ideas from visualizing argumentation and the anchored narratives theory

A knowledge representation architecture for the construction of stories based on interpretation and evidence

This paper describes Stevie, a knowledge representation architecture for the analysis of complex legal cases. Stevie is targeted at legal professionals who may use it to infer stories (plausible and consistent reconstructions of courses of events) from evidence and hypotheses. Stevie is based on known argument ontologies and argumentation logics