Modeling task completion time of in-vehicle information systems while driving with keystroke level modeling

The use of touchscreen-based in-vehicle information systems (IVIS) is increasing. To ensure safe driving, it is important to evaluate IVIS task performance during driving situations. Therefore, we proposed a model to assess the task completion time (TCT) of IVIS tasks while driving using a keystroke-level modeling (KLM) technique. The basic assumptions and heuristic rules of driver behaviors were considered. In addition, based on the characteristics of visual and manual IVIS interactions, we determined the basic unit operators (i.e., visual, manual, and mental operators). User experiments were conducted to determine the individual execution times of unit tasks and to measure the TCT of IVIS tasks while driving. Based on the heuristic rules for model development and individual task execution times, we derive a predictive model for the TCT of IVIS tasks. We used a regression analysis to validate the modeling procedure, showing that the observed TCT was found to have a strong positive correlation with the predicted time from the modeling process. The findings showed that the task completion time needed to perform a secondary task in a driving context can be predicted by KLM. This study provides meaningful insights into the design of touchscreen-based IVIS to enhance driving safety.     

The agreement process: an empirical investigation of human–human computer-mediated collaborative dialogs

In this paper, we investigate the empirical correlates of the agreement process. Informally, the agreement process is the dialog process by which collaborators achieve joint commitment on a joint action. We propose a specific instantiation of the agreement process, derived from our theoretical model, that integrates the IRMA framework for rational problem solving (Bratman, Israel & Pollack, 1988) with Clark's (1992, 1996) work on language as a collaborative activity; and from the characteristics of our task, a simple design problem (furnishing a two-room apartment) in which knowledge is equally distributed among agents, and needs to be shared. The main contribution of our paper is an empirical study of some of the components of the agreement process. We first discuss why we believe the findings from our corpus of computer-mediated dialogs are applicable to human–human collaborative dialogs in general. We then present our theoretical model, and apply it to make predictions about the components of the agreement process. We focus on how information is exchanged in order to arrive at a proposal, and on what constitutes a proposal and its acceptance/rejection. Our corpus study makes use of features of both the dialog and the domain reasoning situation, and led us to discover that the notion of commitment is more useful to model the agreement process than that of acceptance/rejection, as it more closely relates to the unfolding of negotiation.     

From Autonomy to Cooperative Traded Control of Humanoid Manipulation Tasks with Unreliable Communication

In this paper, we present our system design, operational procedure, testing process, field results, and lessons learned for the valve-turning task of the DARPA Robotics Challenge (DRC). We present a software framework for cooperative traded control that enables a team of operators to control a remote humanoid robot over an unreliable communication link. Our system, composed of software modules running on-board the robot and on a remote workstation, allows the operators to specify the manipulation task in a straightforward manner. In addition, we have defined an operational procedure for the operators to manage the teleoperation task, designed to improve situation awareness and expedite task completion. Our testing process, consisting of hands-on intensive testing, remote testing, and remote practice runs , demonstrates that our framework is able to perform reliably and is resilient to unreliable network conditions. We analyze our approach, field tests, and experience at the DRC Trials and discuss lessons learned which may be useful for others when designing similar systems.     

A high-level Petri nets-based approach to verifying task structures

As knowledge-based system technology gains wider acceptance, there is an increasing need to verify knowledge-based systems to improve their reliability and quality. Traditionally, attention has been given to verifying knowledge-based systems at the knowledge level, which only addresses structural errors such as redundancy, conflict and circularity in rule bases. No semantic errors (such as inconsistency at the requirements specification level) have been checked. In this paper, we propose the use of task structures for modeling user requirements and domain knowledge at the requirements specification level, and the use of high-level Petri nets for expressing and verifying the task structure-based specifications. Issues in mapping task structures onto high-level Petri nets are identified, e.g. the representation of task decomposition, constraints and the state model; the distinction between the "follow" and "immediately follow" operators; and the "composition" operator in task structures. The verification of task structures using high-level Petri nets is performed on model specifications of a task through constraint satisfaction and relaxation techniques, and on process specifications of the task based on the reachability property and the notion of specificity     

Task Planning For A Mobile Robot In An Indoor Environment Using Object-oriented Domain Information

For a mobile robot to be practical, it needs to navigate in dynamically changing environments and manipulate objects in the environment with operating ease. The main challenges to satisfying these requirements in mobile robot research include the collection of robot environment information, storage and organization of this information, and fast task planning based on available information. Conventional approaches to these problems are far from satisfactory due to their requirement of high computation time. In this paper, we specifically address the problems of storage and organization of the environment information and fast task planning in the area of robotic research. We propose an special object-oriented data model (OODM) for information storage and management in order to solve the first problem. This model explicitly represents domain knowledge and abstracts a global perspective about the robot's dynamically changing environment. To solve the second problem, we introduce a fast task planning algorithm that fully uses domain knowledge related to robot applications and to the given environment. Our OODM based task planning method presents a general frame work and representation, into which domain specific information, domain decomposition methods and specific path planners can be tailored for different task planning problems. This method unifies and integrates the salient features from various areas such as database, artificial intelligence, and robot path planning, thus increasing the planning speed significantly     

Rialto: A Knowledge Discovery suite for data analysis

A Knowledge Discovery (KD) process is a complex inter-disciplinary task, where different types of techniques coexist and cooperate for the purpose of extracting useful knowledge from large amounts of data. So, it is desirable having a unifying environment, built on a formal basis, where to design and perform the overall process. In this paper we propose a general framework which formalizes a KD process as an algebraic expression, that is, as a composition of operators representing elementary operations on two worlds: the data and the model worlds. Then, we describe a KD platform, named Rialto, based on such a framework. In particular, we provide the design principles of the underlying architecture, highlight the basic features, and provide a number of experimental results aimed at assessing the effectiveness of the design choices.     

Domain-knowledge-guided schema evolution for accounting database systems

The static meta-data view of accounting database management is that the schema of a database is designed before the database is populated and remains relatively fixed over the life cycle of the system. However, the need to support accounting database evolution is clear: a static meta-data view of an accounting database cannot support next generation dynamic environment where system migration, organization reengineering, and heterogeneous system interoperation are essential. This paper presents a knowledge-based approach and mechanism to support dynamic accounting database schema evolution in an object-based data modeling context. When an accounting database schema does not meet the requirements of a firm, the schema must be changed. Such schema evolution can be realized via a sequence of evolution operators. As a result, this paper considers the question: what heuristics and knowledge are necessary to guide a system to choose a sequence of operators to complete a given evolution task for an accounting database? In particular, we first define a set of basic evolution schema operators, employing heuristics to guide the evolution process. Second, we explore how domain-specific knowledge can be used to guide the use of the operators to complete the evolution task. A well-known accounting data model, REA model, is used here to guide the schema evolution process. Third, we discuss a prototype system, REAtool, to demonstrate and test our approach.     

Model‐Based Visualization of Temporal Abstractions

We describe a new conceptual methodology and related computational architecture called Knowledge‐based Navigation of Abstractions for Visualization and Explanation (KNAVE). KNAVE is a domain‐independent framework specific to the task of interpretation, summarization, visualization, explanation, and interactive exploration, in a context‐sensitive manner, of time‐oriented raw data and the multiple levels of higher level, interval‐based concepts that can be abstracted from these data. The KNAVE domain‐independent exploration operators are based on the relations defined in the knowledge‐based temporal‐abstraction problem‐solving method, which is used to abstract the data, and thus can directly use the domain‐specific knowledge base on which that method relies. Thus, the domain‐specific semantics are driving the domain‐independent visualization and exploration processes, and the data are viewed through a filter of domain‐specific knowledge. By accessing the domain‐specific temporal‐abstraction knowledge base and the domain‐specific time‐oriented database, the KNAVE modules enable users to query for domain‐specific temporal abstractions and to change the focus of the visualization, thus reusing for a different task (visualization and exploration) the same domain model acquired for abstraction purposes. We focus here on the methodology, but also describe a preliminary evaluation of the KNAVE prototype in a medical domain. Our experiment incorporated seven users, a large medical patient record, and three complex temporal queries, typical of guideline‐based care, that the users were required to answer and/or explore. The results of the preliminary experiment have been encouraging. The new methodology has potentially broad implications for planning, monitoring, explaining, and interactive data mining of time‐oriented data.     

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.     

Episodic task learning in Markov decision processes

Hierarchical algorithms for Markov decision processes have been proved to be useful for the problem domains with multiple subtasks. Although the existing hierarchical approaches are strong in task decomposition, they are weak in task abstraction, which is more important for task analysis and modeling. In this paper, we propose a task-oriented design to strengthen the task abstraction. Our approach learns an episodic task model from the problem domain, with which the planner obtains the same control effect, with concise structure and much improved performance than the original model. According to our analysis and experimental evaluation, our approach has better performance than the existing hierarchical algorithms, such as MAXQ and HEXQ.     

Approximate knowledge modeling and classification in a frame-based language: The system CAIN

In this article, we present an extension of the frame-based language Objlog+, called CAIN, which allows the homogeneous representation of approximate knowledge (fuzzy, uncertain, and default knowledge) by means of new facets. We developed elements to manage approximate knowledge: fuzzy operators, extension of the inheritance mechanisms, and weighting of structural links. Contrary to other works in the domain, our system is strongly based on a theoretical approach inspired from Zadeh's and Dubois' works. We also defined an original instance classification mechanism, which has the ability to take into account the notions of typicality and similarity as they are presented in the psychological literature. Our model proposes consideration of a particular semantics of default values to estimate the typicality between a class and the instance to classify (ITC). In that way, the possibilities of the typicality representation proposed by frame-based languages are exploited. To find the most appropriate solution we do not systematically choose the most specific class that matches the ITC but we retain the most typical solution. Approximate knowledge is used to make the matching used during the classification process more flexible. Taking into account additional knowledge concerning heuristics and elements of cognitive psychology leads to the enrichment of the classification mechanism. © 2001 John Wiley & Sons, Inc.     

On the use of shared task models in knowledge acquistion,strategic user interaction and clarification agents

In this paper, three different roles of a shared task model as an intermediate representation of a task are presented and illustrated by applications developed in cooperation with industry. First the role of a shared task model in knowledge acquisition is discussed. In one of the two applications, decision support in the domain of soil sanitation, one of the existing generic task models for diagnostic reasoning provided a means to structure knowledge acquisition. In the second application, diagnosis of chemical processes, the acquisition process resulted in a shared task model for diagnostic reasoning on Nylon-6 production. Secondly, the role of a shared task model in designing user interaction is addressed. Three levels of interaction are considered of importance: interaction at the object level, at the level of strategic preferences, and at the level of task modification. In an application in the domain of environmental decision making, this led to the design of a user interface based on the acquired shared task model, within which all three levels of interaction were available to users. Finally, the role of shared task models within a multi-agent system including a clarification agent is addressed. Two software agents were designed that each share a task model with the user: one for a diagnosis task, and one for a clarification task. The shared model of the clarification task reflects the shared task model of diagnosis; clarification includes clarification of the overall diagnostic reasoning process. The multi-agent architecture presented has been developed to support a user both at the level of the diagnostic task he or she is performing and at the level of clarification. The architecture has been applied to the diagnosis of chemical processes.     

Dynamic task allocation for multi-robot search and retrieval tasks

Many application domains require search and retrieval, which is also known in the robotic domain as foraging. For example, in a search and rescue domain, a disaster area needs to be explored and transportation of survivors to a safe area needs to be arranged. Performing such a search and retrieval task by more than one robot increases performance if they are able to distribute their workload efficiently and evenly. In this work, we study the Multi-Robot Task Allocation (MRTA) problem in the search and retrieval domain, where a team of robots is required to cooperatively search for targets of interest in an environment and also retrieve them back to a home base. In comparison with typical foraging tasks, we look at a more general search and retrieval task in which the targets are distinguished with various types, and task allocation also requires taking into account temporal constraints on the team goal. As usual, robots have no prior knowledge about the location of targets in the environment but in addition they need to deliver targets to the home base in a specific order according to their types, which significantly increases the complexity of a foraging problem. We first use a graph-based model to analyse the search and retrieval problem and the dynamics of exploration and retrieval within a cooperative team. We then proceed to present an extended auction-based approach, as well as a prediction approach. The essential difference between these two approaches is that the task allocation and execution procedures in the auction approach are running in parallel, whereas a robot in the prediction approach only needs to choose a task to perform when it has no thing to do. The auction approach uses a winner determination mechanism to allocate tasks to each robot, whereas the robots in the prediction approach implicitly coordinate their activities by team reasoning that leads to consensuses about task allocation. We use the Blocks World for Teams (BW4T) simulator to evaluate the two approaches in our experimental study.     

Learning common behaviors from large sets of unlabeled temporal series

This paper is about extracting knowledge from large sets of videos, with a particular reference to the video-surveillance application domain. We consider an unsupervised framework and address the specific problem of modeling common behaviors from long-term collection of instantaneous observations. Specifically, such data describe dynamic events and may be represented as time series in an appropriate space of features. Starting off from a set of data meaningful of the common events in a given scenario, the pipeline we propose includes a data abstraction level, that allows us to process different data in a homogeneous way, and a behavior modeling level, based on spectral clustering. At the end of the pipeline we obtain a model of the behaviors which are more frequent in the observed scene, represented by a prototypical behavior, which we call a cluster candidate. We report a detailed experimental evaluation referring to both benchmark datasets and on a complex set of data collected in-house. The experiments show that our method compares very favorably with other approaches from the recent literature. In particular the results we obtain prove that our method is able to capture meaningful information and discard noisy one from very heterogeneous datasets with different levels of prior information available.     

An approach based on the domain perspective to develop WSAN applications

As wireless sensor and actuator networks (WSANs) can be used in many different domains, WSAN applications have to be built from two viewpoints: domain and network. These different viewpoints create a gap between the abstractions handled by the application developers, namely the domain and network experts. Furthermore, there is a coupling between the application logic and the underlying sensor platform, which results in platform-dependent projects and source codes difficult to maintain, modify, and reuse. Consequently, the process of developing an application becomes cumbersome. In this paper, we propose a model-driven architecture (MDA) approach for WSAN application development. Our approach aims to facilitate the task of the developers by: (1) enabling application design through high abstraction level models; (2) providing a specific methodology for developing WSAN applications; and (3) offering an MDA infrastructure composed of PIM, PSM, and transformation programs to support this process. Our approach allows the direct contribution of domain experts in the development of WSAN applications, without requiring specific knowledge of programming WSAN platforms. In addition, it allows network experts to focus on the specific characteristics of their area of expertise without the need of knowing each specific application domain.