A narrative-based reasoning with applications in decision support for social service organizations

Nowadays, there is an increasing demand for incorporating unstructured narratives in decision support for knowledge-intensive industries such as healthcare and social service organizations. However, most of the current research on decision support systems (DSS) mainly focused on dealing with structured data and are inadequate to dealing with unstructured narratives such as clients’ records and stories. This paper presents a narrative-based reasoning (NBR) algorithm which incorporates the technologies of knowledge-based system (KBS), computational linguistics, and artificial intelligence (AI) for automatic processing unstructured narratives and inferring useful knowledge for decision support. A NBR enabled DSS has been built and was evaluated through a series of experiments conducted in early intervention of mental health of a social service company in Hong Kong. The performance of NBR was measured based on recall and precision and encouraging results were obtained. High recall and precision are achieved in the reasoning of unstructured data, and high recall is achieved for the association analysis. The results show that it is possible for inferring recommendations for problem solving from unstructured narratives automatically. Based on the approach, it helps to support knowledge workers with reliable suggestions on decision making so as to increase the quality of their solutions.     

Building Bayesian networks for legal evidence with narratives: a case study evaluation

In a criminal trial, evidence is used to draw conclusions about what happened concerning a supposed crime. Traditionally, the three main approaches to modeling reasoning with evidence are argumentative, narrative and probabilistic approaches. Integrating these three approaches could arguably enhance the communication between an expert and a judge or jury. In previous work, techniques were proposed to represent narratives in a Bayesian network and to use narratives as a basis for systematizing the construction of a Bayesian network for a legal case. In this paper, these techniques are combined to form a design method for constructing a Bayesian network based on narratives. This design method is evaluated by means of an extensive case study concerning the notorious Dutch case of the Anjum murders.     

Structured design,consistency analysis and failure reasoning of business workflows with activity-control templates and causal ordering

The design, analysis, control and diagnosis of business workflows have been major challenges for enterprise information system designers. We propose a structured framework for workflow design, formal semantics, consistency analysis, execution automation and failure reasoning targeting E-commerce applications. A business workflow is modeled by using a visual tool named activity-control (AC) diagram. Frequently occurring business procedures are captured by the adoptions of reusable AC templates. With formally defined semantics by a combination of first-order logic and happen-before causal ordering in distributed system theory, workflow consistency can be mechanically analyzed at design time while failure reasoning can be applied at execution time for problem diagnosis. A completely specified model is automatically converted to a workflow by an iterative traversal algorithm that maps an AC diagram to an XML workflow specification which can then be executed automatically by an XML workflow engine. A failure reasoning and diagnosis algorithm is devised to find all possible causes of a failed execution when problems occur. Preliminary proof-of-concept implementation and evaluation results demonstrate the feasibility and effectiveness of our framework and techniques.     

Temporal logic-based deadlock analysis for Ada

A temporal logic-based specification language and deadlock analyzer for Ada is described. The deadlock analyzer is intended for use within Timebench, a concurrent system-design environment with support for Ada. The specification language, COL, uses linear-time temporal logic to provide a formal basis for axiomatic reasoning. The deadlock analysis tool uses the reasoning power of COL to demonstrate that Ada designs specified in COL are systemwide deadlock-free: in essence, it uses a specialized theorem prover to deduce the absence of deadlock. The deadlock algorithm is shown to be decidable for finite systems and acceptable otherwise. It is also shown to have a worst-case computational complexity that is exponential with the number of tasks. The analyzer has been implemented in Prolog. Numerous examples are evaluated using the analyzer, including readers and writers, gas station, five dining philosophers, and a layered communications system. The results indicate that analysis time is reasonable for moderate designs in spite of the worst-case complexity of the algorithm     

Efficiently solving quantified bit-vector formulas

In recent years, bit-precise reasoning has gained importance in hardware and software verification. Of renewed interest is the use of symbolic reasoning for synthesising loop invariants, ranking functions, or whole program fragments and hardware circuits. Solvers for the quantifier-free fragment of bit-vector logic exist and often rely on SAT solvers for efficiency. However, many techniques require quantifiers in bit-vector formulas to avoid an exponential blow-up during construction. Solvers for quantified formulas usually flatten the input to obtain a quantified Boolean formula, losing much of the word-level information in the formula. We present a new approach based on a set of effective word-level simplifications that are traditionally employed in automated theorem proving, heuristic quantifier instantiation methods used in SMT solvers, and model finding techniques based on skeletons/templates. Experimental results on two different types of benchmarks indicate that our method outperforms the traditional flattening approach by multiple orders of magnitude of runtime.     

基于知识表示学习的实时语义数据流推理

传统的语义数据流推理使用前向或后向链式推理产生确定性的答案,但是在复杂的传递规则推理中效率不高,无法满足实时数据流处理场景对答案的及时性要求。因此,提出一种基于联合嵌入模型的知识表示方法,并应用于语义数据流处理中。将规则与事实三元组联合嵌入并利用深度学习模型进行训练,在推理阶段,根据查询中涉及的规则建立推理模板,利用深度学习模型对推理模板产生的三元组进行预测和分类,将结果作为查询和推理答案输出。实验表明,对于复杂规则推理,基于知识表示学习的实时语义数据流推理能够在保障较好推理准确性和命中率的前提下有效地降低延迟。     

Accommodating Practices During Episodes of Disillusionment with Mobile IT

This study investigates how tablet users react when technology falls short of their expectations. We deploy a data/frame model to study this process and investigate resistance-related reactions and the deployment of accommodating practices at the individual level. Analyzing user blogs that provide narratives on user interaction with tablets, we identify triggers of episodes of disillusionment and illustrate five sensemaking paths that users follow, eventually leading to one of three practices: 1) users choose to defer tasks until the situation changes, or they abandon the platform altogether; 2) they develop workarounds at different levels of proficiency; or 3) they proceed by reframing their expectations of the platform. By revealing user decision-making process during episodes of disillusionment, the findings contribute to information systems post-adoption research. At a practical level, the findings inform IT artifact and application design by offering insights on how users process discrepancies between their expectations and actual use experience.

     

SEMANTIC ANALYSIS AND IN-DEPTH UNDERSTANDING OF TECHNICAL TEXTS

We explored the problem of achieving in-depth understanding of natural language sentences from narrative technical reports through knowledge-based free text understanding. We rely on the assumption that texts in an expert domain convey much implicit information, which can be recovered by building and reasoning on a model of the situation described with the help of both linguistic and detailed world knowledge. We describe a two-step approach to semantic analysis: the first step assembles a conceptual representation of a sentence and deals with linguistic issues; the second step actually builds and runs the situational model and is totally dedicated to representation and inference. We evaluated this approach by designing a research prototype that processes sentences from clinical narratives in a medical specialty. This prototype was fully implemented and was tested on actual sentences. We hereby give a detailed account of this implementation as well as the first results obtained.     

Story Albums: Creating Fictional Stories From Personal Photograph Sets

We present a method for the automatic creation of fictional storybooks based on personal photographs. Unlike previous attempts that summarize such collections by picking salient or diverse photos, or creating personal literal narratives, we focus on the creation of fictional stories. This provides new value to users, as well as an engaging way for people (especially children) to experience their own photographs. We use a graph model to represent an artist‐generated story, where each node is a ‘frame’, akin to frames in comics or storyboards. A node is described by story elements, comprising actors, location, supporting objects and time. The edges in the graph encode connections between these elements and provide the discourse of the story. Based on this construction, we develop a constraint satisfaction algorithm for one‐to‐one assignment of nodes to photographs. Once each node is assigned to a photograph, a visual depiction of the story can be generated in different styles using various templates. We show results of several fictional visual stories created from different personal photo sets and in different styles.     

Experience,introspection and expertise: Learning to refine the case-based reasoning process

The case-based reasoning paradigm models how reuse of stored experiences contributes to expertise. In a case-based problem-solver, new problems are solved by retrieving stored information about previous problem-solving episodes and adapting it to suggest solutions to the new problems. The results are then themselves added to the reasoner's memory in new cases for future use. Despite this emphasis on learning from experience, however, experience generally plays a minimal role in models of how the case-based reasoning process is itself performed. Case-based reasoning systems generally do not refine the methods they use to retrieve or adapt prior cases, instead relying on static pre-defined procedures. The thesis of this article is that learning from experience can play a key role in building expertise by refining the case-based reasoning process itself. To support that view and to illustrate the practicality of learning to refine case-based reasoning, this article presents ongoing research into using introspective reasoning about the case-based reasoning process to increase expertise at retrieving and adapting stored cases.     

A Tool For Specification And Verification Of Epistemic Properties In Interpreted Systems

We present a compiler that translates a multi-agent systems specification given in the formalism of Interpreted Systems into an SMV program. We show how an SMV model checker can be coupled with a Kripke model editor (Akka) to allow for the mechanical verification of epistemic properties of multi-agent systems. We apply this methodology to the verification of a communication protocol — the dining cryptographers.     

Integrating predicate transition nets with first order temporal logic in the specification and verification of concurrent systems

This paper presents some results of integrating predicate transition nets with first order temporal logic in the specification and verification of concurrent systems. The intention of this research is to use predicate transition nets as a specification method and to use first order temporal logic as a verification method so that their strengths — the easy comprehension of predicate transition nets and the reasoning power of first order temporal logic can be combined. In this paper, a theoretical relationship between the computation models of these two formalisms is presented; an algorithm for systematically translating a predicate transition net into a corresponding temporal logic system is outlined; and a special temporal refutation proof technique is proposed and illustrated in verifying various concurrent properties of the predicate transition net specification of the five dining philosophers problem.     

A hybrid formal theory of arguments,stories and criminal evidence

This paper presents a theory of reasoning with evidence in order to determine the facts in a criminal case. The focus is on the process of proof, in which the facts of the case are determined, rather than on related legal issues, such as the admissibility of evidence. In the literature, two approaches to reasoning with evidence can be distinguished, one argument-based and one story-based. In an argument-based approach to reasoning with evidence, the reasons for and against the occurrence of an event, e.g., based on witness testimony, are central. In a story-based approach, evidence is evaluated and interpreted from the perspective of the factual stories as they may have occurred in a case, e.g., as they are defended by the prosecution. In this paper, we argue that both arguments and narratives are relevant and useful in the reasoning with and interpretation of evidence. Therefore, a hybrid approach is proposed and formally developed, doing justice to both the argument-based and the narrative-based perspective. By the formalization of the theory and the associated graphical representations, our proposal is the basis for the design of software developed as a tool to make sense of the evidence in complex cases.     

餐饮空间的主题性设计

随着人民生活水平的日益提高,人们在饮食方面所占的消费比例越来越大,对餐饮环境的要求也逐渐提高。而餐饮空间设计作为一种塑造餐饮环境的重要方式,是通过其自身环境设计来满足人们更高层次的精神文化需求。餐饮空间的主题性设计是围绕某种主题思想或以突出某种要素对餐饮整体环境空间进行规划,满足顾客对餐饮的环境需求。     

Minimality in template-guided recombination

Ciliates are unicellular organisms, some of which perform complicated rearrangements of their DNA. Template-guided recombination (TGR) is a formal model for the DNA recombination which occurs in ciliates. TGR has been the subject of much research in formal language theory, as it can be viewed as an operation on formal languages. In TGR, a set of templates serves as a parameter to a language operation which controls which rearrangements can take place; thus, a set of templates is itself a language.Recently, the concept of equivalence in TGR has been considered: given two sets of templates, do they define the same language operation? This paper considers the related question of minimality: given a set of templates T, what is the smallest set of templates (with respect to inclusion) equivalent to T? We show that the minimal set of templates is unique, and consider closure properties and decidability questions related to minimality. We define an operational characterization for equivalence which is useful for results on minimality.     

A new approach to applicable memory-based reasoning

To appropriately utilize the rapidly growing amount of data and information is a big challenge for people and organizations. Standard information retrieval methods, using sequential processing combined with syntax-based indexing and access methods, have not been able to adequately handle this problem. We are currently investigating a different approach, based on a combination of massive parallel processing with case-based (memory-based) reasoning methods. Given the problems of purely syntax-based retrieval methods, we suggest ways of incorporating general domain knowledge into memory-based reasoning. Our approach is related to the properties of the parallel processing microchip MS160, particularly targeted at fast information retrieval from very large data sets. Within this framework different memory-based methods are studied, differing in the type and representation of cases, and in the way that the retrieval methods are supported by explicit general domain knowledge. Cases can be explicitly stored information retrieval episodes, virtually stored abstractions linked to document records, or merely the document records themselves. General domain knowledge can be a multi-relational semantic network, a set of term dependencies and relevances, or compiled into a global similarity metric. This paper presents the general framework, discusses the core issues involved, and describes three different methods illustrated by examples from the domain of medical diagnosis.     

Learning Translation Templates from Bilingual Translation Examples

A mechanism for learning lexical correspondences between two languages from sets of translated sentence pairs is presented. These lexical level correspondences are learned using analogical reasoning between two translation examples. Given two translation examples, the similar parts of the sentences in the source language must correspond to the similar parts of the sentences in the target language. Similarly, the different parts must correspond to the respective parts in the translated sentences. The correspondences between similarities and between differences are learned in the form of translation templates. A translation template is a generalized translation exemplar pair where some components are generalized by replacing them with variables in both sentences and establishing bindings between these variables. The learned translation templates are obtained by replacing differences or similarities by variables. This approach has been implemented and tested on a set of sample training datasets and produced promising results for further investigation.