Confidence-Based Reasoning with Local Temporal Formal Contexts

Formal Concept Analysis (FCA) is a theory whose goal is to discover and to extract Knowledge from qualitative data. It provides tools for reasoning with implication basis (and association rules). In this paper we analyse how to apply FCA reasoning to increase confidence in sports betting, by means of detecting temporal regularities from data. It is applied to build a Knowledge based system for confidence reasoning.Ministerio de Ciencia e Innovación TIN2009-09492Junta de Andalucía TIC-606

Conceptual graph-based knowledge representation for supporting reasoning in African traditional medicine

Although African patients use both conventional or modern and traditional healthcare simultaneously, it has been proven that 80% of people rely on African traditional medicine (ATM). ATM includes medical activities stemming from practices, customs and traditions which were integral to the distinctive African cultures. It is based mainly on the oral transfer of knowledge, with the risk of losing critical knowledge. Moreover, practices differ according to the regions and the availability of medicinal plants. Therefore, it is necessary to compile tacit, disseminated and complex knowledge from various Tradi-Practitioners (TP) in order to determine interesting patterns for treating a given disease. Knowledge engineering methods for traditional medicine are useful to model suitably complex information needs, formalize knowledge of domain experts and highlight the effective practices for their integration to conventional medicine. The work described in this paper presents an approach which addresses two issues. First it aims at proposing a formal representation model of ATM knowledge and practices to facilitate their sharing and reusing. Then, it aims at providing a visual reasoning mechanism for selecting best available procedures and medicinal plants to treat diseases. The approach is based on the use of the Delphi method for capturing knowledge from various experts which necessitate reaching a consensus. Conceptual graph formalism is used to model ATM knowledge with visual reasoning capabilities and processes. The nested conceptual graphs are used to visually express the semantic meaning of Computational Tree Logic (CTL) constructs that are useful for formal specification of temporal properties of ATM domain knowledge. Our approach presents the advantage of mitigating knowledge loss with conceptual development assistance to improve the quality of ATM care (medical diagnosis and therapeutics), but also patient safety (drug monitoring)

A Graph-Based Semantics Workbench for Concurrent Asynchronous Programs

A number of novel programming languages and libraries have been proposed that offer simpler-to-use models of concurrency than threads. It is challenging, however, to devise execution models that successfully realise their abstractions without forfeiting performance or introducing unintended behaviours. This is exemplified by SCOOP---a concurrent object-oriented message-passing language---which has seen multiple semantics proposed and implemented over its evolution. We propose a "semantics workbench" with fully and semi-automatic tools for SCOOP, that can be used to analyse and compare programs with respect to different execution models. We demonstrate its use in checking the consistency of semantics by applying it to a set of representative programs, and highlighting a deadlock-related discrepancy between the principal execution models of the language. Our workbench is based on a modular and parameterisable graph transformation semantics implemented in the GROOVE tool. We discuss how graph transformations are leveraged to atomically model intricate language abstractions, and how the visual yet algebraic nature of the model can be used to ascertain soundness.Comment: Accepted for publication in the proceedings of FASE 2016 (to appear

Discovering Implicational Knowledge in Wikidata

Knowledge graphs have recently become the state-of-the-art tool for representing the diverse and complex knowledge of the world. Examples include the proprietary knowledge graphs of companies such as Google, Facebook, IBM, or Microsoft, but also freely available ones such as YAGO, DBpedia, and Wikidata. A distinguishing feature of Wikidata is that the knowledge is collaboratively edited and curated. While this greatly enhances the scope of Wikidata, it also makes it impossible for a single individual to grasp complex connections between properties or understand the global impact of edits in the graph. We apply Formal Concept Analysis to efficiently identify comprehensible implications that are implicitly present in the data. Although the complex structure of data modelling in Wikidata is not amenable to a direct approach, we overcome this limitation by extracting contextual representations of parts of Wikidata in a systematic fashion. We demonstrate the practical feasibility of our approach through several experiments and show that the results may lead to the discovery of interesting implicational knowledge. Besides providing a method for obtaining large real-world data sets for FCA, we sketch potential applications in offering semantic assistance for editing and curating Wikidata

Reasoning About the Reliability of Multi-version, Diverse Real-Time Systems

This paper is concerned with the development of reliable real-time systems for use in high integrity applications. It advocates the use of diverse replicated channels, but does not require the dependencies between the channels to be evaluated. Rather it develops and extends the approach of Little wood and Rush by (for general systems) by investigating a two channel system in which one channel, A, is produced to a high level of reliability (i.e. has a very low failure rate), while the other, B, employs various forms of static analysis to sustain an argument that it is perfect (i.e. it will never miss a deadline). The first channel is fully functional, the second contains a more restricted computational model and contains only the critical computations. Potential dependencies between the channels (and their verification) are evaluated in terms of aleatory and epistemic uncertainty. At the aleatory level the events ''A fails" and ''B is imperfect" are independent. Moreover, unlike the general case, independence at the epistemic level is also proposed for common forms of implementation and analysis for real-time systems and their temporal requirements (deadlines). As a result, a systematic approach is advocated that can be applied in a real engineering context to produce highly reliable real-time systems, and to support numerical claims about the level of reliability achieved

Selecting Attributes for Sport Forecasting using Formal Concept Analysis

In order to address complex systems, apply pattern recongnition on their evolution could play an key role to understand their dynamics. Global patterns are required to detect emergent concepts and trends, some of them with qualitative nature. Formal Concept Analysis (FCA) is a theory whose goal is to discover and to extract Knowledge from qualitative data. It provides tools for reasoning with implication basis (and association rules). Implications and association rules are usefull to reasoning on previously selected attributes, providing a formal foundation for logical reasoning. In this paper we analyse how to apply FCA reasoning to increase confidence in sports betting, by means of detecting temporal regularities from data. It is applied to build a Knowledge-Based system for confidence reasoning.Comment: Paper 3 for the Complex Systems in Sports Workshop 2011 (CS-Sports 2011

Surveying human habit modeling and mining techniques in smart spaces

A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field

Bounded Rationality for Data Reasoning based on Formal Concept Analysis

Formal Concept Analysis (FCA) is a theory whose goal is to discover and extract Knowledge from qualitative data. It also provides tools for sound reasoning (implication basis and association rules). The aim of this paper is to apply FCA to a new model for bounded rationality based on the implicational reasoning over contextual knowledge bases which are obtained from contextual selections. A contextual selection is a selection of events and attributes about them which induces partial contexts from a global formal context. In order to avoid inconsistencies, association rules are selected as reasoning engine. The model is applied to forecast sport results.Ministerio de Ciencia e Innovación TIN2009-09492Junta de Andalucía TIC-606

Qualitative Reasoning on Complex Systems from Observations

A hybrid approach to phenomenological reconstruction of Complex Systems (CS), using Formal Concept Analysis (FCA) as main tool for conceptual data mining, is proposed. To illustrate the method, a classic CS is selected (cellular automata), to show how FCA can assist to predict CS evolution under different conceptual descriptions (from different observable features of the CS).Junta de Andalucía TIC-606

Plan stability: replanning versus plan repair

The ultimate objective in planning is to construct plans for execution. However, when a plan is executed in a real environment it can encounter differences between the expected and actual context of execution. These differences can manifest as divergences between the expected and observed states of the world, or as a change in the goals to be achieved by the plan. In both cases, the old plan must be replaced with a new one. In replacing the plan an important consideration is plan stability. We compare two alternative strategies for achieving the {em stable} repair of a plan: one is simply to replan from scratch and the other is to adapt the existing plan to the new context. We present arguments to support the claim that plan stability is a valuable property. We then propose an implementation, based on LPG, of a plan repair strategy that adapts a plan to its new context. We demonstrate empirically that our plan repair strategy achieves more stability than replanning and can produce repaired plans more efficiently than replanning