Experience-Based Cognition for Driving Behavioral Fingerprint Extraction

Abstract

With the rapid progress of information technologies, cars have been made increasingly intelligent. This allows cars to act as cognitive agents, i.e., to acquire knowledge and understanding of the driving habits and behavioral characteristics of drivers (i.e., driving behavioral fingerprint) through experience. Such knowledge can be then reused to facilitate the interaction between a car and its driver, and to develop better and safer car controls. In this paper, we propose a novel approach to extract the driver’s driving behavioral fingerprints based on our conceptual framework Experience-Oriented Intelligent Things (EOIT). EOIT is a learning system that has the potential to enable Internet of Cognitive Things (IoCT) where knowledge can be extracted from experience, stored, evolved, shared, and reused aiming for cognition and thus intelligent functionality of things. By catching driving data, this approach helps cars to collect the driver’s pedal and steering operations and store them as experience; eventually, it uses obtained experience for the driver’s driving behavioral fingerprint extraction. The initial experimental implementation is presented in the paper to demonstrate our idea, and the test results show that it outperforms the Deep Learning approaches (i.e., deep fully connected neural networks and recurrent neural networks/Long Short-Term Memory networks).     

Adding Intelligence to Cars Using the Neural Knowledge DNA

ABSTRACT

In this paper, we propose a Neural Knowledge DNA (NK-DNA)-based framework that is capable of learning from the car’s daily operations and reusing such learned knowledge in future tasks. The NK-DNA is a novel knowledge representation and reasoning approach designed to support discovering, storing, reusing, improving, and sharing knowledge among machines and computing devices. We examine our framework for drivers’ classification based on their driving behaviors. The experimental data are collected via smartphone sensors. The initial results are presented, and the direction for our future research is defined.     

The Neural Knowledge DNA Based Smart Internet of Things

Abstract

The Internet of Things (IoT) has gained significant attention from industry as well as academia during the past decade. Smartness, however, remains a substantial challenge for IoT applications. Recent advances in networked sensor technologies, computing, and machine learning have made it possible for building new smart IoT applications. In this paper, we propose a novel approach: the Neural Knowledge DNA based Smart Internet of Things that enables IoT to extract knowledge from past experiences, as well as to store, evolve, share, and reuse such knowledge aiming for smart functions. By catching decision events, this approach helps IoT gather its own daily operation experiences, and it uses such experiences for knowledge discovery with the support of machine learning technologies. An initial case study is presented at the end of this paper to demonstrate how this approach can help IoT applications become smart: the proposed approach is applied to fitness wristbands to enable human action recognition.     

Experience-Oriented Intelligence for Internet of Things

ABSTRACT

The Internet of Things (IoT) has gained significant attention from industry as well as academia during the past decade.The main reason behind this interest is the capabilities of the IoT for seamlessly integrating classical networks and networked objects, and hence allowing people to create an intelligent environment based on this powerful integration. However, how to extract useful information from data produced by IoT and facilitate standard knowledge sharing among different IoT systems are still open issues to be addressed. In this paper, we propose a novel approach, the Experience-Oriented Smart Things (EOST), that utilizes deep learning and knowledge representation concept called Decisional DNA to help IoT systems acquire, represent, and store knowledge, as well as share it amid various domains where it can be required to support decisions. Decisional DNA motivation stems from the role of deoxyribonucleic acid (DNA) in storing and sharing information and knowledge. We demonstrate our approach in a set of experiments, in which the IoT systems use knowledge gained from past experience to make decisions and predictions. The presented initial results show that the EOST is a very promising approach for knowledge capture, representation, sharing, and reusing in IoT systems.     

Acquiring object-knowledge

Abstract

A novel approach to interactively acquire knowledge about new objects in a logic environment is presented. When the user supplies an unknown fact containing unknown objects (constants), the system will ask interesting membership and existential queries about the objects. The answers to these questions allow the system to update its knowledge base. Two basic strategies are implemented: one that examines existing Horn clauses for the predicate and another one that uses types. Furthermore, a powerful heuristic based on analogy, to pose the most interesting questions first, is presented.     

Future Event Prediction Based on Temporal Knowledge Graph Embedding

Accurate prediction of future events brings great benefits and reduces losses for society in many domains, such as civil unrest, pandemics, and crimes. Knowledge graph is a general language for describing and modeling complex systems. Different types of events continually occur, which are often related to historical and concurrent events. In this paper, we formalize the future event prediction as a temporal knowledge graph reasoning problem. Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process. As a result, they cannot effectively reason over temporal knowledge graphs and predict events happening in the future. To address this problem, some recent works learn to infer future events based on historical event-based temporal knowledge graphs. However, these methods do not comprehensively consider the latent patterns and influences behind historical events and concurrent events simultaneously. This paper proposes a new graph representation learning model, namely Recurrent Event Graph ATtention Network (RE-GAT), based on a novel historical and concurrent events attention-aware mechanism by modeling the event knowledge graph sequence recurrently. More specifically, our RE-GAT uses an attention-based historical events embedding module to encode past events, and employs an attention-based concurrent events embedding module to model the associations of events at the same timestamp. A translation-based decoder module and a learning objective are developed to optimize the embeddings of entities and relations. We evaluate our proposed method on four benchmark datasets. Extensive experimental results demonstrate the superiority of our RE-GAT model comparing to various baselines, which proves that our method can more accurately predict what events are going to happen.     

Applying Decisional DNA to Internet of Things: The Concept and Initial Case Study

In this article, we present a novel approach utilizing Decisional DNA to help the Internet of Things capture decisional events and reuse them for decision making in future operations. The Decisional DNA is a domain-independent, standard and flexible knowledge representation structure that allows its domains to acquire, store, and share experiential knowledge and formal decision events in an explicit way. We apply this approach to our current work—SmartBike, a sensor-equipped bicycle built under the concept of Internet of Things. By using Decisional DNA and machine learning algorithms, the SmartBike is able to distinguish its user's patterns based on past riding data. The presented conceptual approach demonstrates how Decisional DNA can be applied to the Internet of Things and bring to them intelligence required by forthcoming semantic networks.     

Experience-Based Decisional DNA (DDNA) to Support Product Development

ABSTRACT

Knowledge and experience are important requirements for product development. The aim of this paper is to propose a systematic approach for industrial product development. This approach uses smart knowledge management system comprising of set of experience knowledge structure and decisional DNA (DDNA) along with virtual engineering tools (virtual engineering object, virtual engineering process, and virtual engineering factory). This system provides a new direction to researchers working on product development, especially designers and manufacturers. It will reduce their communication gap by allowing them to work on the same platform. The proposed system adopts an early consideration of manufacturing issues. Therefore, it can shorten product development cycle time, minimize overall development cost, and ensure a smooth transition into production. The proposed system is dynamic in nature because it updates itself after every time a new decision related to product development activity is made. Product development process can be performed systematically and efficiently using this system as it stores knowledge of experiences of different activities.     

Facebook communities about nostalgic photos of Turkey: creative practices of remembering and representing the past

ABSTRACT

This article focuses on Facebook communities about nostalgic photos of Turkey to explore how citizenship is enacted through the participatory and collaborative use of social media to remember and represent the past. By sharing their personal photos, knowledge, testimonies, narratives and life stories, members of these communities actively and creatively use social media to generate new ways of remembering and representing the past, as well as improving its accessibility and visibility. Furthermore, through exchanging affectively and politically charged photos and conversations about the past, participants fashion nostalgia as a public feeling that becomes a source for affective political criticism of the present. This article addresses the participatory and collaborative creation of knowledge and memory of the past to discuss everyday creative citizenship practices facilitated by social media.     

基于神经网络结构学习的知识求精方法

知识求精是知识获取中必不可少的步骤．已有的用于知识求精的ＫＢＡＮＮ（ｋｎｏｗ ｌｅｄｇｅ ｂａｓｅｄ ａｒｔｉｆｉｃｉａｌｎｅｕｒａｌｎｅｔｗ ｏｒｋ）方法,主要局限性是训练时不能改变网络的拓扑结构．文中提出了一种基于神经网络结构学习的知识求精方法,首先将一组规则集转化为初始神经网络,然后用训练样本和结构学习算法训练初始神经网络,并提取求精的规则知识．网络拓扑结构的改变是通过训练时采用基于动态增加隐含节点和网络删除的结构学习算法实现的．大量实例表明该方法是有效的     

A Reduction Algorithm Meeting Users Requirements

Generally a database encompasses various kinds of knowledge and is shared by many users.Different users may prefer different kinds of knowledge.So it is important for a data mining algorithm to output specific knowledge according to users‘ current requirements (preference).We call this kind of data mining requirement-oriented knowledge discovery (ROKD).When the rough set theory is used in data mining,the ROKD problem is how to find a reduct and corresponding rules interesting for the user.Since reducts and rules are generated in the same way,this paper only concerns with how to find a particular reduct.The user‘s requirement is described by an order of attributes,called attribute order,which implies the importance of attributes for the user.In the order,more important attributes are located before less important ones.then the problem becomes how to find a reduct including those attributes anterior in the attribute order.An approach to dealing with such a problem is proposed.And its completeness for reduct is proved.After that,three kinds of attribute order are developed to describe various user requirements.     

Progress in the Development of National Knowledge Infrastructure

This paper presents the recent process in a long-term research project,called National Knowledge Infrastructure(or NKI).Initiated in the early 2000,the project aims to develop a multi-domain shareable knowledge base for knowledge-intensive applications.To develop NKI,we have used domain-specific ontologies as a solid basis,and have built more than 600 ontologies.Using these ontologies and our knowledge acquisition methods,we have extracted about 1.1 millions of domain assertions.For users to access our NKI knowledge,we have developed a uniform multi-modal human-knowledge interface.We have also implemented a knowledge application programming interface for various applications to share the NKI knowledge.     

Generic screen representations for future-proof systems,is it possible?: There is more to a GUI than meets the eye

BackgroundFuture-proof EHR systems must be capable of interpreting information structures for medical concepts that were not available at the build-time of the system. The two-model approach of CEN 13606/openEHR using archetypes achieves this by separating generic clinical knowledge from domain-related knowledge. The presentation of this information can either itself be generic, or require design time awareness of the domain knowledge being employed.ObjectiveTo develop a Graphical User Interface (GUI) that would be capable of displaying previously unencountered clinical data structures in a meaningful way.MethodsThrough “reasoning by analogy” we defined an approach for the representation and implementation of “presentational knowledge”. A proof-of-concept implementation was built to validate its implementability and to test for unanticipated issues.ResultsA two-model approach to specifying and generating a screen representation for archetype-based information, inspired by the two-model approach of archetypes, was developed. There is a separation between software-related display knowledge and domain-related display knowledge and the toolkit is designed with the reuse of components in mind.ConclusionsThe approach leads to a flexible GUI that can adapt not only to information structures that had not been predefined within the receiving system, but also to novel ways of displaying the information. We also found that, ideally, the openEHR Archetype Definition Language should receive minor adjustments to allow for generic binding.     

A generalized-constraint neural network model: Associating partially known relationships for nonlinear regressions

In an attempt to enhance the neural network technique so that it can evolve from a “black box” tool into a semi-analytical one, we propose a novel modeling approach of imposing “generalized constraints” on a standard neural network. We redefine approximation problems by use of a new formalization with the aim of embedding prior knowledge explicitly into the model to the maximum extent. A generalized-constraint neural network (GCNN) model has therefore been developed, which basically consists of two submodels. One is constructed by the standard neural network technique to approximate the unknown part of the target function. The other is formed from partially known relationships to impose generalized constraints on the whole model. Three issues arising after combination of the two submodels are discussed: (a) the better approximation provided by the GCNN model compared with a standard neural network, (b) the identifiability of parameters in the partially known relationships, and (c) the discrepancy in the approximation due to removable singularities in the target function. Numerical studies of three benchmark problems show important findings that have not previously been reported in the literature. Significant benefits were observed from using the GCNN model in comparison with a standard neural network.     

mwKAT: A meta-tool-generated knowledge acquisition tool for multimedia workstations development

mwKAT is an interactive knowledge acquisition tool for acquiring domain knowledge about multimedia components. It constructs knowledge bases for a consulting system that produces the design specification for a multimedia workstation according to the user requirements.mwKAT is generated from and executed inGAS, a primitives-based generic knowledge acquisition meta-tool. It contains three acquisition primitives, namely, parameter proposing, constraint proposing, and fix proposing to construct an intermediate knowledge base represented by a dependency model. These primitives identify necessary domain knowledge and guide users to propose significant components, constraints, and fix methods into the dependency model.mwKAT also invokes knowledge verification and validation primitives to verify the completeness, consistency, compilability, and correctness of the intermediate knowledge base.     

Automated fuzzy knowledge acquisition with connectionist adaptation

This paper presents an automated knowledge acquisition architecture for the truck docking problem. The architecture consists of a neural network block, a fuzzy rule generation block and a genetic optimisation block. The neural network block is used to quickly and adaptively learn from trials the driving knowledge. The fuzzy rule generation block then extracts the driving knowledge to form a knowledge rule base. The driving knowledge rule base is further optimised in the genetic optimisation block using a genetic algorithm. Computer simulations are presented to show the effectiveness of the architecture.     

Domain-driven KDD for mining functionally novel rules and linking disjoint medical hypotheses

IntroductionAn important quality of association rules is novelty. However, evaluating rule novelty is AI-hard and has been a serious challenge for most data mining systems.ObjectiveIn this paper, we introduce functional novelty, a new non-pairwise approach to evaluating rule novelty. A functionally novel rule is interesting as it suggests previously unknown relations between user hypotheses.MethodsWe developed a novel domain-driven KDD framework for discovering functionally novel association rules. Association rules were mined from cardiovascular data sets. At post-processing, domain knowledge-compliant rules were discovered by applying semantic-based filtering based on UMLS ontology. Their knowledge compliance scores were computed against medical knowledge in Pubmed literature. A cardiologist explored possible relationships between several pairs of unknown hypotheses. The functional novelty of each rule was computed based on its likelihood to mediate these relationships.ResultsHighly interesting rules were successfully discovered. For instance, common rules such as diabetes mellitus?coronary arteriosclerosis was functionally novel as it mediated a rare association between von Willebrand factor and intracardiac thrombus.ConclusionThe proposed post-mining domain-driven rule evaluation technique and measures proved to be useful for estimating candidate functionally novel rules with the results validated by a cardiologist.     

Hierarchy on multigranulation structures: a knowledge distance approach

Abstract

In this paper, a knowledge distance approach is proposed to study the hierarchy under the frame of multigranulation. In our approach, the finest granulation structure is considered as the frame of reference, and then a knowledge distances algebraic lattice is constructed. Through such algebraic lattice, a partial order is derived, which can be used to characterize the finer or coarser relationships among multigranulation structures. It is also shown that the uncertainty measurements are monotonic in terms of the obtained partial order.     

Autonomous Agents that Learn to Better Coordinate

A fundamental difficulty faced by groups of agents that work together is how to efficiently coordinate their efforts. This coordination problem is both ubiquitous and challenging, especially in environments where autonomous agents are motivated by personal goals.Previous AI research on coordination has developed techniques that allow agents to act efficiently from the outset based on common built-in knowledge or to learn to act efficiently when the agents are not autonomous. The research described in this paper builds on those efforts by developing distributed learning techniques that improve coordination among autonomous agents.The techniques presented in this work encompass agents who are heterogeneous, who do not have complete built-in common knowledge, and who cannot coordinate solely by observation. An agent learns from her experiences so that her future behavior more accurately reflects what works (or does not work) in practice. Each agent stores past successes (both planned and unplanned) in their individual casebase. Entries in a casebase are represented as coordinated procedures and are organized around learned expectations about other agents.It is a novel approach for individuals to learn procedures as a means for the group to coordinate more efficiently. Empirical results validate the utility of this approach. Whether or not the agents have initial expertise in solving coordination problems, the distributed learning of the individual agents significantly improves the overall performance of the community, including reducing planning and communication costs.     

Domain-specific formal ontology of archaeology and its application in knowledge acquisition and analysis

Inherent heterogeneity and distribution of knowledge strongly prevent knowledge from sharing and reusing among different agents and software entities, and a formal ontology has been viewed as a promising means to tackle this problem. In this paper, a domain-specific formal ontology of archaeology is presented. The ontology mainly consists of three parts: archaeological categories, their relationships and axioms. The ontology not only captures the semantics of archaeological knowledge, but also provides archaeology with an explicit and forma specification of a shared conceptualization, thus making archaeological knowledge shareable and reusable across humans and machines in a structured fashion. Further, we propose a method to verify ontology correctness based on the individuals of categories. As applications of the ontology, we have developed an ontology-driven approach to knowledge acquisition from archaeological text and a question answering system for archaeological knowledge.