FAILURE MODE EFFECTS ANALYSIS: A PRACTICAL APPLICATION OF FUNCTIONAL MODELING

Knowledge of how a device works is important for many tasks. Yet, systems that attempt to base their reasoning on the use of a functional model fail to capture such knowledge or only capture it implicitly. Instead they rely solely on the knowledge of the purpose of the system and provide causal explanations of how this purpose is achieved. This type of model only represents knowledge of what the system is for, not how the system works. However, engineers also rely on knowledge of how a device works to complete tasks successfully. One such task is failure mode effects analysis (FMEA). FMEA involves investigation and assessment of the effects of all possible failure modes on a system. This process is both tedious and time consuming, and it requires detailed expert knowledge of the system under consideration, including information about the structure of the system and its purpose or function. This means that any attempt to automate the whole of the FMEA process must involve both the structural and functional levels. This paper reviews the FMEA process and considers the requirements of an automated FMEA system. It outlines a prototype FMEA system and presents a functional modeling system that relies on the results produced by an underlying structural simulator.     

Practical abduction: characterization,decomposition and concurrency

Abstract

Abductive inferences seem to be ubiquitous in cognition, and cognitive agents often solve complex abduction tasks very rapidly. However, abduction characterized as ‘inference to the best explanation’ is in general computationally intractable. This paper describes three related ideas for understanding how intelligent agents might efficiently perform abduction tasks. First, we recharacterize the abduction task as inference to a confident explanation, where a confident explanation is internally consistent, parsimonious, distinctly more plausible than alternative explanations, and explains as much of the data as possible with high confidence. Second, we describe a decomposition of the task of synthesizing a confident explanation into several subtasks so that the synthesis starts from islands of relative certainty and then grows opportunistically. This decomposition helps in controlling the computational cost of accommodating interactions among explanatory hypotheses, especially incompatibility interactions. Third, we present a concurrent mechanism for synthesizing confident explanations. The mechanism exploits data and processing dependencies afforded by the decomposition of the synthesis task. The emphasis of this approach to abduction is on characterizing the constraints of the abduction task and exploiting these constraints for making abductive inferences. In describing this approach, we also clarify the precise class of abduction problems addressed by the RED-2 system, and report on some new experiments. The main result is a computational model that not only enables efficient abductive inferences but also accommodates explanatory interactions, uncertainty, and data collection.     

Learning with video‐based examples – Are you sure you do not need help?

This study investigated help‐seeking activities in a computer‐based environment teaching argumentative skills by videos of argumentative dialogues of teachers who discussed controversy issues in the context of a workshop. Learners, all of them students of educational sciences, solved learning tasks on the presented argumentative dialogues and reflected on their response certitude. Forty‐three students voluntary took part in the study. Two experimental groups varied according to the type of task they solved. Group 1 got adjunct questions, so‐called self‐explanation prompts that elicited elaboration of the video content. Group 2 answered multiple‐choice tasks that assessed the same knowledge. After each task, participants of both groups (a) had to judge the certainty of their response being correct (i.e., the marking of the multiple‐choice task or the written self‐explanations) and (b) were offered to use the help function on demand. Results revealed the relevance of learners' response certitude with respect to their help use. Low response certitude about the correctness of a task solution led to higher help use which was positively related to learning outcome. However, learners' response certitude was unrelated to the actual correctness of their task solution. Type of task had no influence on response certitude, help use or learning outcome.     

FUNCTIONAL REPRESENTATION AND REASONING FOR REFLECTIVE SYSTEMS

Functional models have been extensively investigated in the context of several problemsolving tasks such as device diagnosis and design. In this paper, we view problem solvers themselves as devices, and use structure-behavior-function models to represent how they work. The model representing the functioning of a problem solver explicitly specifies how the knowledge and reasoning of the problem solver result in the achievement of its goals. Then, we employ these models for performance-driven reflective learning. We view performance-driven learning as the task of redesigning the knowledge and reasoning of the problem solver to improve its performance. We use the model of the problem solver to monitor its reasoning. Assign blame when it fails, and appropriately redesign its knowledge and reasoning. This paper focuses on the model-based redesign of a path planner's task structure. It illustrates the modelbased reflection using examples from an operational system called the Autognostic system.     

CaSE: Explaining Text Classifications by Fusion of Local Surrogate Explanation Models with Contextual and Semantic Knowledge

Generating explanations within a local and model-agnostic explanation scenario for text classification is often accompanied by a local approximation task. In order to create a local neighborhood for a document, whose classification shall be explained, sampling techniques are used that most often treat the according features at least semantically independent from each other. Hence, contextual as well as semantic information is lost and therefore cannot be used to update a human’s mental model within the according explanation task. In case of dependent features, such explanation techniques are prone to extrapolation to feature areas with low data density, therefore causing misleading interpretations. Additionally, the ”the whole is greater than the sum of its parts” phenomenon is disregarded when using explanations that treat the according words independently from each other. In this paper, an architecture named CaSE is proposed that either uses Semantic Feature Arrangements or Semantic Interrogations to overcome these drawbacks. Combined with a modified version of Local interpretable model-agnostic explanations (LIME), a state of the art local explanation framework, it is capable of generating meaningful and coherent explanations. The approach utilizes contextual and semantic knowledge from unsupervised topic models in order to enable realistic and semantic sampling and based on that generate understandable explanations for any text classifier. The key concepts of CaSE that are deemed essential for providing humans with high quality explanations are derived from findings of psychology. In a nutshell, CaSE shall enable Semantic Alignment between humans and machines and thus further improve the basis for Interactive Machine Learning. An extensive experimental validation of CaSE is conducted, showing its effectiveness by generating reliable and meaningful explanations whose elements are made of contextually coherent words and therefore are suitable to update human mental models in an appropriate way. In the course of a quantitative analysis, the proposed architecture is evaluated w.r.t. a consistency property and to Local Fidelity of the resulting explanation models. According to that, CaSE generates more realistic explanation models leading to higher Local Fidelity compared to LIME.     

嵌入式系统裸机的任务调度应用设计

在中低端 MCU的嵌入式系统软件设计中,为了节省有限的RAM资源,只能采用不加操作系统的裸机方式.为了借鉴操作系统的任务调度机制,在裸机开发方式中,设计一种不带任务堆栈的逻辑任务,按照具体应用划分若干逻辑任务,这些逻辑任务共享一个系统堆栈,每个逻辑任务都有自己的事件队列和任务处理程序,任务之间通过发送事件的形式进行通信.这种方案既避免了加载操作系统对系统RAM资源和MCU计算资源的消耗,又能够实现类似于操作系统的任务调度机制,实现软件的模块化,从而设计出低耦合、高内聚的软件.     

A framework for knowledge-based temporal abstraction

A new domain-independent knowledge-based inference structure is presented, specific to the task of abstracting higher-level concepts from time-stamped data. The framework includes a model of time, parameters, events and contexts. A formal specification of a domain's temporal abstraction knowledge supports acquisition, maintenance, reuse and sharing of that knowledge.

The knowledge-based temporal abstraction method decomposes the temporal abstraction task into five subtasks. These subtasks are solved by five domain-independent temporal abstraction mechanisms. The temporal abstraction mechanisms depend on four domain-specific knowledge types: structural, classification (functional), temporal semantic (logical) and temporal dynamic (probabilistic) knowledge. Domain values for all knowledge types are specified when a temporal abstraction system is developed.

The knowledge-based temporal abstraction method has been implemented in the RÉSUMÉ system and has been evaluated in several clinical domains (protocol-based care, monitoring of children's growth and therapy of diabetes) and in an engineering domain (monitoring of traffic control), with encouraging results.     

Evolving Soccer Keepaway Players Through Task Decomposition

Complex control tasks can often be solved by decomposing them into hierarchies of manageable subtasks. Such decompositions require designers to decide how much human knowledge should be used to help learn the resulting components. On one hand, encoding human knowledge requires manual effort and may incorrectly constrain the learners hypothesis space or guide it away from the best solutions. On the other hand, it may make learning easier and enable the learner to tackle more complex tasks. This article examines the impact of this trade-off in tasks of varying difficulty. A space laid out by two dimensions is explored: (1) how much human assistance is given and (2) how difficult the task is. In particular, the neuroevolution learning algorithm is enhanced with three different methods for learning the components that result from a task decomposition. The first method, coevolution, is mostly unassisted by human knowledge. The second method, layered learning, is highly assisted. The third method, concurrent layered learning, is a novel combination of the first two that attempts to exploit human knowledge while retaining some of coevolutions flexibility. Detailed empirical results are presented comparing and contrasting these three approaches on two versions of a complex task, namely robot soccer keepaway, that differ in difficulty of learning. These results confirm that, given a suitable task decomposition, neuroevolution can master difficult tasks. Furthermore, they demonstrate that the appropriate level of human assistance depends critically on the difficulty of the problem.Editor Robert Holte     

A systematic review and taxonomy of explanations in decision support and recommender systems

With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems. A key requirement for the success and adoption of such systems is that users must trust system choices or even fully automated decisions. To achieve this, explanation facilities have been widely investigated as a means of establishing trust in these systems since the early years of expert systems. With today’s increasingly sophisticated machine learning algorithms, new challenges in the context of explanations, accountability, and trust towards such systems constantly arise. In this work, we systematically review the literature on explanations in advice-giving systems. This is a family of systems that includes recommender systems, which is one of the most successful classes of advice-giving software in practice. We investigate the purposes of explanations as well as how they are generated, presented to users, and evaluated. As a result, we derive a novel comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems. The taxonomy includes a variety of different facets, such as explanation objective, responsiveness, content and presentation. Moreover, we identified several challenges that remain unaddressed so far, for example related to fine-grained issues associated with the presentation of explanations and how explanation facilities are evaluated.     

An Evaluation of the Potential of Task Analysis in the Evolution of Interactive Work Systems

Developed forms of task analysis allow designers to focus on both utility and usability issues in the development of interactive work systems. The models they generate represent aspects of the human, computer and domain elements of an interactive work system. Many interactive work systems are embedded in an organisational context. Pressure for changes are present in this context and provide impetus to stakeholders to change work tasks and the supporting tools. Interactive work systems also provide evolutionary pressures of their own, changing the very task they were designed to support. One approach to coping with change has been to evolve interactive work systems. Currently none of these techniques place focus on the performance of tasks as central, and consideration of usability is minimal. However, an evolutionary design approach forces an evolutionary experience upon users, and we cannot be sure whether this approach enhances the user’s experience or degrades their performance. Given the strength of task analysis it is likely that it will be applied within evolutionary contexts. Yet, little work has been undertaken to examine whether its role will, or could be different. We ask how we can move task analysis towards being used in a principled manner in the evolution of interactive work systems. This paper examines a number of features of the approach called task knowledge structures that may be useful in evolving interactive work systems. We look at tasks and their representativeness, roles, goals, objects (their attributes, relationships, typicality and centrality) and actions. We present a developing framework for examining other task analysis approaches for their utility in supporting interactive work systems evolution. Finally, we discuss future work within the area of applying task analysis in the evolution of interactive work systems.     

Argument‐based explanation of the British nationality act as a logic program

Explanations are a significant component of any knowledge‐based system in the legal domain. We have previously proposed a method by which explanations can be improved by making use of annotations on program clauses as to the role of the clause, and organising the explanation according to an argument schema based on that of Stephen Toulmin. In this paper we describe an application of this approach to a part of the British Nationality Act. This serves to illustrate both the practicality of making the required annotations on a legal logic program, and the gains in terms of explanation that can be achieved.     

Incremental acquisition of search knowledge

The development of highly effective heuristics for search problems is a difficult and time-consuming task. We present a knowledge acquisition approach to incrementally model expert search processes. Though, experts do not normally have complete introspective access to that knowledge, their explanations of actual search considerations seem very valuable in constructing a knowledge-level model of their search processes.Furthermore, for the basis of our knowledge acquisition approach, we substantially extend the work done on Ripple-down rules which allows knowledge acquisition and maintenance without analysis or a knowledge engineer. This extension allows the expert to enter his domain terms during the KA process; thus the expert provides a knowledge-level model of his search process. We call this framework nested ripple-down rules.Our approach targets the implicit representation of the less clearly definable quality criteria by allowing the expert to limit his input to the system to explanations of the steps in the expert search process. These explanations are expressed in our search knowledge interactive language. These explanations are used to construct a knowledge base representing search control knowledge. We are acquiring the knowledge in the context of its use, which substantially supports the knowledge acquisition process. Thus, in this paper, we will show that it is possible to build effective search heuristics efficiently at the knowledge level. We will discuss how our system SmS1.3 (SmS for Smart Searcher) operates at the knowledge level as originally described by Newell. We complement our discussion by employing SmS for the acquisition of expert chess knowledge for performing a highly pruned tree search. These experimental results in the chess domain are evidence for the practicality of our approach.     

How should I explain? A comparison of different explanation types for recommender systems

Recommender systems help users locate possible items of interest more quickly by filtering and ranking them in a personalized way. Some of these systems provide the end user not only with such a personalized item list but also with an explanation which describes why a specific item is recommended and why the system supposes that the user will like it. Besides helping the user understand the output and rationale of the system, the provision of such explanations can also improve the general acceptance, perceived quality, or effectiveness of the system.In recent years, the question of how to automatically generate and present system-side explanations has attracted increased interest in research. Today some basic explanation facilities are already incorporated in e-commerce Web sites such as Amazon.com. In this work, we continue this line of recent research and address the question of how explanations can be communicated to the user in a more effective way.In particular, we present the results of a user study in which users of a recommender system were provided with different types of explanation. We experimented with 10 different explanation types and measured their effects in different dimensions. The explanation types used in the study include both known visualizations from the literature as well as two novel interfaces based on tag clouds. Our study reveals that the content-based tag cloud explanations are particularly helpful to increase the user-perceived level of transparency and to increase user satisfaction even though they demand higher cognitive effort from the user. Based on these insights and observations, we derive a set of possible guidelines for designing or selecting suitable explanations for recommender systems.     

The role of explanations in an intelligent assistant system

This paper sets out to illustrate the importance of transparency within software support systems and in particular for those intelligent assistant systems performing complex industrial design tasks. Such transparency (with the meaning ‘clear’ or ‘easy to understand’) can be achieved by two distinct strategies that complement each other:

• 1.(i) The design of intelligible systems that would avoid the need for in depth explanation.
• 2.(ii) The flexible generation of those definitions or aspects of the system or domain that remain ambiguous.

The paper illustrates that for the generation of useful explanations going beyond a simple justification of a problem solving trace, specific explanatory knowledge must be acquired. By itself the problem solving techniques are not sufficient. A new approach to acquire and model explanatory knowledge for software systems is presented. The new four-layer explanatory model can be used to determine the range of explanation suitable for a given systems domain. This model has been successfully used for the development of an explanation component for the design assistant system ASSIST that supports factory layout planning, in itself a complex design task.     

Developing an explanation component for a knowledge-based system: Discussion

The articles in this special issue cover a wide range, and represent many different conceptions of “explanation” and what explanation research should be about. In this short discussion article, I will address some of the implications of this work for those trying to develop a practical explanation system. In particular I will focus on how explanation needs are determined, how detailed explanation content is chosen, and how the explanations provided relate to the problem solving activities of the system.     

An ontological approach to designers’ idea explanation style: Towards supporting the sharing of kansei-ideas in textile design

In human-centered design activities, each designer has his or her own ideas about emotional aspect (or kansei) of new products. It is a key issue to share this vague kansei-idea appropriately at the earliest stage of design activities. This paper shows a novel ontological engineering approach to support kansei-idea sharing. The approach focuses on an idea explanation style as the wisdom of the design team. Ontological engineering has been making contributions to systematize knowledge and vocabulary by modeling them. Needless to say, it is difficult to model the vague kansei-idea itself. However, if the modeled object is shifted from the kansei-idea to the kansei-idea explanation style, it can provide the benefit of modeling. We investigated the effectiveness of the ontological engineering approach, and concluded that to construct an ontological framework of designers’ explanations is especially useful regarding these points: clarification of the essence of the explanation style, discovery of problems in explanations, and analyzing difficulties in acquiring explanation style for novices. From the investigation, what we can support and how a support system should be designed became clear. Furthermore, we built a kansei-idea sharing support system, and obtained the results of its initial trials.     

Incremental learning of tasks from user demonstrations, past experiences, and vocal comments.

Since many years the robotics community is envisioning robot assistants sharing the same environment with humans. It became obvious that they have to interact with humans and should adapt to individual user needs. Especially the high variety of tasks robot assistants will be facing requires a highly adaptive and user-friendly programming interface. One possible solution to this programming problem is the learning-by-demonstration paradigm, where the robot is supposed to observe the execution of a task, acquire task knowledge, and reproduce it. In this paper, a system to record, interpret, and reason over demonstrations of household tasks is presented. The focus is on the model-based representation of manipulation tasks, which serves as a basis for incremental reasoning over the acquired task knowledge. The aim of the reasoning is to condense and interconnect the data, resulting in more general task knowledge. A measure for the assessment of information content of task features is introduced. This measure for the relevance of certain features relies both on general background knowledge as well as task-specific knowledge gathered from the user demonstrations. Beside the autonomous information estimation of features, speech comments during the execution, pointing out the relevance of features are considered as well. The results of the incremental growth of the task knowledge when more task demonstrations become available and their fusion with relevance information gained from speech comments is demonstrated within the task of laying a table.