Modeling driver behavior in a cognitive architecture

OBJECTIVE: This paper explores the development of a rigorous computational model of driver behavior in a cognitive architecture--a computational framework with underlying psychological theories that incorporate basic properties and limitations of the human system. BACKGROUND: Computational modeling has emerged as a powerful tool for studying the complex task of driving, allowing researchers to simulate driver behavior and explore the parameters and constraints of this behavior. METHOD: An integrated driver model developed in the ACT-R (Adaptive Control of Thought-Rational) cognitive architecture is described that focuses on the component processes of control, monitoring, and decision making in a multilane highway environment. RESULTS: This model accounts for the steering profiles, lateral position profiles, and gaze distributions of human drivers during lane keeping, curve negotiation, and lane changing. CONCLUSION: The model demonstrates how cognitive architectures facilitate understanding of driver behavior in the context of general human abilities and constraints and how the driving domain benefits cognitive architectures by pushing model development toward more complex, realistic tasks. APPLICATION: The model can also serve as a core computational engine for practical applications that predict and recognize driver behavior and distraction.     

A cognitive robot architecture based on tactile and visual information

In this paper, we propose an architecture for a cognitive robot based on tactile and visual information. Visual information contains various features such as location and area of various colored regions. Most of these features are irrelevant for object recognition to achieve the given task. In the architecture, tactile information plays a key role in the selection of visual features and discritization of selected features. In order to find appropriate visual features we use a correlation coefficient between the values of the features and action series. Then the ChiMerge algorithm is employed to discritize the value of the selected feature into a small number of intervals. Consequently, quantization of a state space to accomplish the given task is achieved. An appropriate behavior to the given task is acquired by using this state space with reinforcement learning algorithm. We give experimental results of computer simulation to show the validity of our method.     

A cognitive architecture that solves a problem stated by Minsky

We named "Minsky's problem" the challenge of building up a cognitive architecture able to perform a good diagnosis based on multiple criteria that arrive one by one as successive clues. This is a remarkable human information processing capability, and a desirable ability for an artificial expert system. We present a general cognitive design that solves Minsky's problem and a neural network implementation of it that uses distributed associative memories. The type of architecture we present is based on the interaction between an "attribute-object associator (AOA)" and an "intersection filter (IF)" of successive evoked objects, with the intermediation of a working (short-term) memory.     

Deep learning based robot cognitive architecture for collaborative assembly tasks

As the manufacturing industry becomes more agile, the use of collaborative robots capable of safely working with humans is becoming more prevalent, while adaptable and natural interaction is a goal yet to be achieved. This work presents a cognitive architecture composed of perception and reasoning modules that allows a robot to adapt its actions while collaborating with humans in an assembly task. Human action recognition perception is performed using convolutional neural network models with inertial measurement unit and skeleton tracking data. The action predictions are used for task status reasoning which predicts the time left for each action in a task allowing a robot to plan future actions. The task status reasoning uses a recurrent neural network method which is developed for transferability to new actions and tasks. Updateable input parameters allowing the system to optimise for each user and task with each trial performed are also investigated. Finally, the complete system is demonstrated with the collaborative assembly of a small chair and wooden box, along with a solo robot task of stacking objects performed when it would otherwise be idle. The human actions recognised are using a screw driver, Allen key, hammer and hand screwing, with online accuracies between 83–92%. User trials demonstrate the robot deciding when to start collaborative actions in order to synchronise with the user, as well as deciding when it has time to complete an action on its solo task before a collaborative action is required.     

Visual modeling for Web 2.0 applications using model driven architecture approach

Mashup is now a major Web 2.0 technology because, rather than simply aggregating Web content, it combines various Web resources from other websites. Considering Web 2.0 mashups from a software engineering perspective, a visual modeling approach is crucial. Current Web 2.0 developments, however, do not support a conceptual modelling for Web 2.0 applications. This leads to inefficient development processes and a low potential for reuse. The Unified Modeling Language (UML) is a general-purpose modeling language that may be used in widely varying application domains. However, UML often lacks elements for modeling and representing concrete concepts of specific domains such as Web 2.0 mashup modeling. To address these shortcomings, this study proposes Web2.0MUML, a Model Driven Architecture (MDA) approach and UML profile for modeling Web 2.0 mashups. The Web2.0MUML profile extends UML by using a profile mechanism for Web 2.0 mashup modeling that presents the relevant structural properties of Web 2.0 at the conceptual level.     

Visual modeling for scientific software architecture design. A practical approach

This paper presents the problem of Molecular Beam Epitaxy and Reflection High-Energy Electron Diffraction with the help of a unified, modern MDA approach. Model-Driven Architecture (MDA) constitutes a modern and unusually efficient method of improving the process of generating software. It was created at the beginning of the twenty-first century by the Object Management Group as an element of Model-Driven Development, a highly promoted trend in software engineering. In MDA a viewpoint on a system is a technique for abstraction using a selected set of architectural concepts and structuring rules, in order to focus on particular concerns within a system. In MDA, system design begins with defining the problem domain. Next, at a highly abstract level—independent of the system and programming platform—a Platform-Independent Model (PIM) is constructed as well as a general system specification. This specification is created with the help of Unified Modeling Language. The real implementation of the system is performed through the transformation of PIM to Platform-Specific Model (PSM). The essence of Model-Driven Architecture is the replacement of the twentieth century approach to programming, calling that “everything is an object”, to the modern—“everything is a model”.     

Optimized mobile cloud resource discovery architecture based on dynamic cognitive and intelligent technique

In current world, the Mobile Cloud Computing (MCC) will undergo a dramatic upgrade with integrating of mobile cloud resources in small regions to provide a better MCC service. This paper considers the MCC resource availability problem in integrated mobile cloud environment to optimization resources availability with a better effectiveness, accuracy, reliable, low latency and low complexity. The DCICRD architecture is proposed in this paper optimizes the resource discover process with better resource availability. The proposed DCICRD architecture runs various operations such Resource demand prediction to find the required level of resources, Cloudlet Resource Discovery process which discovers resources based on the requirement predicted using two states expand and shrink. The expand state is used to discover resources on-demand in resource scarcity situation and the shrink state is called when abundant resource available to the required level.  Further, Resource reliability check is performed to identify the reliable resource with energy level and signal strength above the threshold level. Finally, Local Resource Information Management Table is used to store the local resource provider information and Central Resource Information Management Table is used to store all local resource information for handover of resource provider. The implementation and evaluation is conducted by comparing the HARD architecture. The comparison result shows that proposed DCICRD architecture is better than HARD architecture by optimizing the resource discovery process and produce reliable resources on demand as per the required level with a better effectiveness using expand and shrink state, accuracy by making resource available for required level, reliable by sending message for frequent checking of Resource with required energy level and signal strength, low latency by making resource available locally and reduce complexity by performing only on-demand resource discovery operation after initial startup. Thus, the proposed DCICRD architecture is better when compared to HARD architecture.     

Visual attention in driving: the effects of cognitive load and visual disruption

OBJECTIVE: This study investigates the effect of cognitive load on guidance of visual attention. BACKGROUND: Previous studies have shown that cognitive load can undermine driving performance, particularly drivers' ability to detect safety-critical events. Cognitive load combined with the loss of exogenous cues, which can occur when the driver briefly glances away from the roadway, may be particularly detrimental. METHOD: In each of two experiments, twelve participants engaged in an auditory task while performing a change detection task. A change blindness paradigm was implemented to mask exogenous cues by periodically blanking the screen in a driving simulator while a change occurred. Performance measures included participants' sensitivity to vehicle changes and confidence in detecting them. RESULTS: Cognitive load uniformly diminished participants' sensitivity and confidence, independent of safety relevance or lack of exogenous cues. Periodic blanking, which simulated glances away from the road-way, undermined change detection to a greater degree than did cognitive load; however, drivers' confidence in their ability to detect changes was diminished more by cognitive load than by periodic blanking. CONCLUSION: Cognitive load and short glances away from the road are additive in their tendency to increase the likelihood of drivers missing safety-critical events. APPLICATION: This study demonstrates the need to consider the combined consequence of cognitive load and brief glances away from the road in the design of emerging in-vehicle devices and the need to provide drivers with better feedback regarding these consequences.     

Computational modeling of human performance in multiple monitor environments with ACT-R cognitive architecture

Much research on modeling human performance associated with visual perception is formulated by schematic models based on neural mechanisms or cognitive architectures. But, these two modeling paradigms are limited in the domains of multiple monitor environments. Although the schematic model based on neural mechanisms can represent human visual systems in multiple monitor environments by providing a detailed account of eye and head movements, these models cannot easily be applied in complex cognitive interactions. On the other hand, the cognitive architectures can model the interaction of multiple aspects of cognition, but these architectures have not focused on modeling the visual orienting behavior of eye and head movements. Thus, in this study, a specific cognitive architecture, which is ACT-R, is extended by an existing schematic model of human visual systems based on neural mechanisms in order to model human performance in multiple monitor environments more accurately. And, this study proposes a method of modeling human performance using the extended ACT-R. The proposed method is validated by an experiment, confirming that the proposed method is able to predict human performance more accurately in multiple monitor environments.Relevance to industryPredicting human performance with a computational model can be used as an alternative method to implementing iterative user testing for developing a system interface. The computational model in this study can predict human performance in multiple monitor environments, so that the model can be applied early on in the design phase, to evaluate the system interface in multiple monitor environments.     

Visual tools for business model innovation: Recommendations from a cognitive perspective

Managers involved in business model innovation (BMI) encounter a series of cognitive challenges. Although cognition research provides important insights into how visual tools can reduce cognitive challenges, it does not address the effective design of business model tools. To advance our understanding in this area, this research applies a cognition‐centered perspective to analyze different visual business model representations. It builds upon a systematic literature review that identifies a selection of 45 visual representations from the academic literature and a sample of 50 visualizations from outside the academic realm. A content analysis of the sample reveals that all visual business model representations can be classified into three complementary categories, leading to a novel framework for distinguishing business model understandings. After assigning each visual representation to the framework, we use findings from the cognition literature to derive recommendations on how to select suitable graphic forms for different phases of the BMI process. Thus, this research contributes to the broader understanding of how visual tools can support business model innovation at a cognitive level.     

Visual syntax does matter: improving the cognitive effectiveness of the i* visual notation

Goal-oriented modelling is one of the most important research developments in the requirements engineering (RE) field. This paper conducts a systematic analysis of the visual syntax of i*, one of the leading goal-oriented languages. Like most RE notations, i* is highly visual. Yet surprisingly, there has been little debate about or modification to its graphical conventions since it was proposed more than a decade ago. We evaluate the i* visual notation using a set of principles for designing cognitively effective visual notations (the Physics of Notations). The analysis reveals some serious flaws in the notation together with some practical recommendations for improvement. The results can be used to improve its effectiveness in practice, particularly for communicating with end users. A broader goal of the paper is to raise awareness about the importance of visual representation in RE research, which has historically received little attention.     

Applying a fusion of wearable sensors and a cognitive inspired architecture to real-time ergonomics analysis of manual assembly tasks

High value manufacturing systems still require ergonomically intensive manual activities. Examples include the aerospace industry where the fitting of pipes and wiring into confined spaces in aircraft wings is still a manual operation. In these environments, workers are subjected to ergonomically awkward forces and postures for long periods of time. This leads to musculoskeletal injuries that severely limit the output of a shopfloor leading to loss of productivity. The use of tools such as wearable sensors could provide a way to track the ergonomics of workers in real time. However, an information processing architecture is required in order to ensure that data is processed in real time and in a manner that meaningful action points are retrieved for use by workers.In this work, based on the Adaptive Control of Thought—Rational (ACT-R) cognitive framework, we propose a Cognitive Architecture for Wearable Sensors (CAWES); a wearable sensor system and cognitive architecture that is capable of taking data streams from multiple wearable sensors on a worker’s body and fusing them to enable digitisation, tracking and analysis of human ergonomics in real time on a shopfloor. Furthermore, through tactile feedback, the architecture is able to inform workers in real time when ergonomics rules are broken. The architecture is validated through the use of an aerospace case study undertaken in laboratory conditions. The results from the validation are encouraging and in the future, further tests will be performed in an actual working environment.     

基于认知模型的室内移动服务机器人人机耦合协同作业机制

针对老年人和残疾人这类特殊用户群体与服务机器人构成的人机智能系统,提出了基于ACT-R(理性思维的适应性控制)认知架构模型的室内移动服务机器人人机耦合协同作业机制.基于ACT-R认知架构对人机一体化室内移动服务机器人人机协同作业系统进行了总体设计,利用简单自然的人机效应通道,设计了基于ACT-R认知架构的人机耦合界面;通过人-机-环境空间感知耦合,提出并建立了室内移动服务机器人人机一体化协同决策作业机制.最后在室内环境下进行移动服务机器人人机协同作业实验,系统安全高效地完成了作业任务,验证了该机制的有效性.     

图形的视觉联想及视觉张力

视觉张力的形成主要取决于视觉元素的形状、大小、位置以及空间关系的处理,它产生于形态的夸张变形及奇特的表现。     

Equipping the ACT-R cognitive architecture with a temporal ratio model of memory and using it in a new intelligent adaptive interface

ACT-R, as a useful and well-known cognitive architecture, is a theory for simulating and understanding human cognition. However, the standard version of this architecture uses a deprecated forgetting model. So, we equipped it with a temporal ratio model of memory that has been named as SIMPLE (Scale-Independent Memory, Perception, and Learning). On the other hand, one of the usages of cognitive architectures is to model the user in an Intelligent Adaptive Interface (IAI) implementation. Thus, our motivation for this effort is to use this equipped ACT-R in an IAI to deliver the right information at the right time to users based on their cognitive needs. So, to test our proposed equipped ACT-R, we designed and implemented a new IAI to control a swarm of Unmanned Aerial Vehicles (UAVs). This IAI uses the equipped ACT-R for user cognitive modeling, to deliver the right information to the users based on their forgetting model. Thus, our contributions are: equipping the ACT-R cognitive architecture with the SIMPLE memory model and using this equipped version of ACT-R for user modeling in a new IAI to control a group of UAVs. Simulation results, which have been obtained using different subjective and objective measures, show that we significantly improved situation awareness of the users using the IAI empowered by our equipped ACT-R.

     

Usability and software architecture

The role of software architecture with respect to usability has evolved over the past 20 years. The architectures of the 1980s and early 1990s assumed that usability was primarily a property of the presentation of information. Therefore, simply separating the presentation from the dialogue and application made it easy to modify that presentation after user testing. A more popular belief in the 1990s was that usability concerns greatly affected system functionality as well as the presentation. This emphasis took attention away from architectural support (beyond separation). Achieving the correct functionality for a given system became paramount. It is our observation that even if presentation and functionality of a system are well designed, the usability of a system can be greatly compromised if the underlying architecture does not support human concerns beyond modifiability. This paper will present a new role for software architecture in usability, preliminary research and practice stemming from this role and a research agenda for the future.     

Precision architecture

The processor component of the Hewlett-Packard Precision Architecture system is described. The architecture's goals, how the architecture addresses the spectrum of general-purpose user information processing needs, and some architectural design tradeoffs are examined. Extendibility and longevity features are considered     

基于Agent的体系结构

目前的软件开发方法需要开发人员具有较丰富的计算机和程序设计经验,而且需要知道每一个软构件的详细信息。为软件开发提供了一个基于Agent的体系结构,主要包括构件的识别、构件功能的识别和构件集成,结构能自动识别和集成可重用软构件,不需要熟练的程序设计人员或系统开发人员就可以开发软件,因为通过Agent的使用,应用开发是自动的,结果不但具有较高的理论价值,而且具有很大的实践指导意义。