Supporting reasoning with different types of evidence in intelligence analysis

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 acquiring, evaluating and interpreting information in collaboration with others. Agents assist analysts in reasoning with different types of evidence to identify what happened and why, what is credible, and how to obtain further evidence. Argumentation schemes lie at the heart of the tool, and sense-making agents assist analysts in structuring evidence and identifying plausible hypotheses. A crowdsourcing agent is used to reason about structured information explicitly obtained from groups of contributors, and provenance is used to assess the credibility of hypotheses based on the origins of the supporting information

Resilience, reliability, and coordination in autonomous multi-agent systems

Acknowledgements The research reported in this paper was funded and supported by various grants over the years: Robotics and AI in Nuclear (RAIN) Hub (EP/R026084/1); Future AI and Robotics for Space (FAIR-SPACE) Hub (EP/R026092/1); Offshore Robotics for Certification of Assets (ORCA) Hub (EP/R026173/1); the Royal Academy of Engineering under the Chair in Emerging Technologies scheme; Trustworthy Autonomous Systems “Verifiability Node” (EP/V026801); Scrutable Autonomous Systems (EP/J012084/1); Supporting Security Policy with Effective Digital Intervention (EP/P011829/1); The International Technology Alliance in Network and Information Sciences.Peer reviewedPostprin

Representational transformations : using maps to write essays

This research was supported by NSERC (The Natural Sciences and Engineering Research Council of Canada) RGPIN-2020-04401 and EPSRC (Engineering and Physical Sciences Research Council) EP/T518062/1.Essay-writing is a complex, cognitively demanding activity. Essay-writers must synthesise source texts and original ideas into a textual essay. Previous work found that writers produce better essays when they create effective intermediate representations. Diagrams, such as concept maps and argument maps, are particularly effective. However, there is insufficient knowledge about how people use these intermediate representations in their essay-writing workflow. Understanding these processes is critical to inform the design of tools to support workflows incorporating intermediate representations. We present the findings of a study, in which 20 students planned and wrote essays. Participants used a tool that we developed, Write Reason, which combines a free-form mapping interface with an essay-writing interface. This let us observe the types of intermediate representations participants built, and crucially, the process of how they used and moved between them. The key insight is that much of the important cognitive processing did not happen within a single representation, but instead in the processes that moved between multiple representations. We label these processes `representational transformations'. Our analysis characterises key properties of these transformations: cardinality, explicitness, and change in representation type. We also discuss research questions surfaced by the focus on transformations, and implications for tool designers.Publisher PDFPeer reviewe

El análisis de inteligencia: técnicas de análisis y fuentes de error. Una aproximación desde la teoría argumentativa

Tradicionalmente se ha entendido que la principal función del razonamiento humano es procesar y valorar información a partir de las percepciones y el conocimiento previo; mejorar la cognición individual o gestionar lo nuevo y anticipar el futuro. Sin embargo, recientes estudios apuntan a que el razonamiento habría evolucionado y se habría adaptado para la argumentación y su principal función sería discutir; ganar el debate; imponer las propias ideas; y justificar acciones pasadas, con independencia de que la conclusión alcanzada durante la argumentación sea lógica o correcta. En este trabajo se plantean explicaciones a los sesgos cognitivos que influyen en el analista de Inteligencia desde estas recientes teorías y se proponen posibles remedios y técnicas para utilizarlos a favor de un mejor análisis. También se insiste en la importancia de la intuición

How we designed winning algorithms for abstract argumentation and which insight we attained

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 fundamental reference in computational argumentation. The algorithms of ArgSemSAT are based on multiple calls to a SAT solver to compute complete labellings, and on encoding constraints to drive the search towards the solution of decision and enumeration problems. In this paper we focus on preferred semantics (and incidentally stable as well), one of the most popular and complex semantics for identifying acceptable arguments. We discuss our design methodology that includes a systematic exploration and empirical evaluation of labelling encodings, algorithmic variations and SAT solver choices. In designing the successful ArgSemSAT, we discover that: (1) there is a labelling encoding that appears to be universally better than other, logically equivalent ones; (2) composition of different techniques such as AllSAT and enumerating stable extensions when searching for preferred semantics brings advantages; (3) injecting domain specific knowledge in the algorithm design can lead to significant improvements

Reasoning in criminal intelligence analysis through an argumentation theory-based framework

This thesis provides an in-depth analysis of criminal intelligence analysts’ analytical reasoning process and offers an argumentation theory-based framework as a means to support that reasoning process in software applications. Researchers have extensively researched specific areas of criminal intelligence analysts’ sensemaking and reasoning processes over the decades. However, the research is fractured across different research studies and those research studies often have high-level descriptions of how criminal intelligence analysts formulate their rationale (argument). This thesis addresses this gap by offering low level descriptions on how the reasoning-formulation process takes place. It is presented as a single framework, with supporting templates, to inform the software implementation process. Knowledge from nine experienced criminal intelligence analysts from West Midlands Police and Belgium’s Local and Federal Police forces were elicited through a semi-structured interview for study 1 and the Critical Decision Method (CDM), as part of the Cognitive Task Analysis (CTA) approach, was used for study 2 and study 3. The data analysis for study 1 made use of the Qualitative Conventional Content Analysis approach. The data analysis for study 2 made use of a mixed method approach, consisting out of Qualitative Directed Content Analysis and the Emerging Theme Approach. The data analysis for study 3 made use of the Qualitative Directed Content Analysis approach. The results from the three studies along with the concepts from the existing literature informed the construction of the argumentation theory-based framework. The evaluation study for the framework’s components made use of Paper Prototype Testing as a participatory design method over an electronic medium. The low-fidelity prototype was constructed by turning the frameworks’ components into software widgets that resembled widgets on a software application’s toolbar. Eight experienced criminal intelligence analysts from West Midlands Police and Belgium’s Local and Federal Police forces took part in the evaluation study. Participants had to construct their rationale using the available components as part of a simulated robbery crime scenario, which used real anonymised crime data from West Midlands Police force. The evaluation study made use of a Likert scale questionnaire to capture the participant’s views on how the frameworks’ components aided participants with; understanding what was going on in the analysis, lines-of-enquiry and; the changes in their level of confidence pertaining to their rationale. A non-parametric, one sample z-test was used for reporting the statistical results. The significance is at 5% (α=0.05) against a median of 3 for the z-test, where μ =3 represents neutral. The participants reported a positive experience with the framework’s components and results show that the framework’s components aided them with formulating their rationale and understanding how confident they were during different phases of constructing their rationale