Accountable system design architecture for embodied AI: a focus on physical human support robots

Although the development of robot-based support systems for elderly people has become more popular, it is difficult for humans to understand the actions, plans, and behavior of autonomous robots and the reasons behind them, particularly when the robots include learning algorithms. Learning-based autonomous systems which are called AI are treated as an inherently untrustworthy ‘black box,’ because machine learning or deep learning algorithms are difficult for humans to understand. Robot systems such as assistive robots, which work closely with humans, however, should be trusted. Systems should therefore achieve accountability for all stakeholders. However, most research in this field has focused on particular systems and situations, and no general design architecture exists. In this study, we propose a new design method, focused on accountability and transparency, for learning-based robot systems. Describing the entire system is a necessary first step, and transcribing the described system for each stakeholder based on several principles is effective for achieving accountability. The method improves transparency for systems, including learning algorithms. A standing assistive robot is used as an example of the entire system to clarify which system parts require greater transparency. This study adopted the Systems Modeling Language (SysML) to describe the system and the described system is used for the information representation. Information should be represented considering the relationships between stakeholders, information, and the system interface. Because of their complexity, it is difficult for humans to understand the complete set of information available in robot systems. Systems should therefore present only the information required, depending on the situation. The stakeholder–interface relationship is also important because it is more beneficial for professionals to view information relevant to their specialized field, which would be difficult for others to understand. By contrast, the interface should be intuitive for general users. Visualization and sound are very useful means of transmitting information, with advantages and disadvantages for different circumstances. These relationships are important for achieving accountability. Finally, we show an example of implementation with a developed support system. It is confirmed that accountable systems can be designed based on the proposed design architecture.     

Model decomposition and reduction tools for large-scale networks in systems biology

Biological system models are routinely developed in modern systems biology research following appropriate modelling/experiment design cycles. Frequently these take the form of high-dimensional nonlinear Ordinary Differential Equations that integrate information from several sources; they usually contain multiple time-scales making them difficult even to simulate. These features make systems analysis (understanding of robust functionality) — or redesign (proposing modifications in order to improve or modify existing functionality) a particularly hard problem. In this paper we use concepts from systems theory to develop two complementary tools that can help us understand the complex behaviour of such system models: one based on model decomposition and one on model reduction. Our aim is to algorithmically produce biologically meaningful, simplified models, which can then be used for further analysis and design. The tools presented are applied on a model of the Epidermal Growth Factor signalling pathway.     

Optimal control of distributed multiagent systems with finite-time group flocking

The flocking of multiple intelligent agents, inspired by the swarm behavior of natural phenomena, has been widely used in the engineering fields such as in unmanned aerial vehicle (UAV) and robots system. However, the performance of the system (such as response time, network throughput, and resource utilization) may be greatly affected while the intelligent agents are engaged in cooperative work. Therefore, it is concerned to accomplish the distributed cooperation while ensuring the optimal performance of the intelligent system. In this paper, we investigated the optimal control problem of distributed multiagent systems (MASs) with finite-time group flocking movement. Specifically, we propose two optimal group flocking algorithms of MASs with single-integrator model and double-integrator model. Then, we study the group consensus of distributed MASs by using modern control theory and finite-time convergence theory, where the proposed optimal control algorithms can drive MASs to achieve the group convergence in finite-time while minimizing the performance index of the intelligence system. Finally, experimental simulation shows that MASs can keep the minimum energy function under the effect of optimal control algorithm, while the intelligent agents can follow the optimal trajectory to achieve group flocking in finite time.     

Safety and Security in Multiagent Systems: Report on the 2nd SASEMAS workshop (SASEMAS'05)

As intelligent autonomous agents and multiagent systems' applications become more pervasive, it becomes increasingly more important to understand the risks associated with using these systems. Incorrect or inappropriate agent behaviour can have harmful effects including financial cost, loss of data, and injury to humans or systems. Thus, security and safety are two central issues when developing and deploying such systems.However, the process of developing safe and secure multiagent systems, and verifying and validating them, is much more difficult than for conventional software systems. This is due to many agent-related aspects, such as the complex and rich multiagent environments, the risks involved in such environments, and the characteristics that can be found in agent systems such as learning, dynamic reacting and adapting. Hence, new and different techniques and perspectives are required to assist with the development and deployment of such systems.The Safety and Security in Multiagent Systems (SASEMAS) workshop presents new developments, and lessons learned from real world cases, and it provides a forum for the exchange of ideas and discussion on areas related to security and safety in multiagent systems.     

The effects of viscosity in choice and refusal IPD environments

An objective of multi-agent systems is to build robust intelligent systems capable of existing in complex environments. These systems are often characterised as being uncertain and open to change which make such systems far more difficult to design and understand. Some of this uncertainty and change occurs in open agent environments where agents can freely enter and exit the system. In this paper we will examine this form of population change in a game theoretic setting. These simulations involve studying population change through a number of alternative viscosity models. The simulations will examine two possible trust models. All our simulations will use a simple choice and refusal game environment within which agents may freely choose with which of their peers to interact.     

Reference Resolution Challenges for Intelligent Agents: The Need for Knowledge

This paper presents a vision of how language-endowed, next-generation intelligent agents might resolve—that is, fully interpret—references to objects and events in language input. It describes some of the more difficult reference phenomena that are not being sufficiently treated by practical systems and suggests what kinds of knowledge must be available to intelligent agents to enable them to reach human competence in reference resolution.     

Toward a computational interpretation of situation semantics1

Situation semantics proposes novel and attractive treatments for several problem areas of natural language semantics, such as efficiency (context sensitivity) and prepositional attitude reports. Its focus on the information carried by utterances makes the approach very promising for accounting for pragmatic phenomena. However, situation semantics seems to oppose several basic assumptions underlying current approaches to natural language processing and the design of intelligent systems in general. It claims that efficiency undermines the standard notions of logical form, entailment, and proof theory, and objects to the view that mental processes necessarily involve internal representations. The paper attempts to clarify these issues and discusses the impact of situation semantics’ criticisms for natural language processing, knowledge representation, and reasoning. I claim that the representational approach is the only currently practical one for the design of large intelligent systems, but argue that the representations used should be efficient in order to account for the system's embedding in its environment. The paper concludes by stating some constraints that a computational interpretation of situation semantics should obey and discussing remaining problems.     

Applying Computer Modeling to Examine Complex Dynamics and Pattern Formation of Tissue Growth

For research in areas such as developmental biology and cancer, understanding the formation and regrowth of tissue is of great importance. Since complex system behavior makes it difficult to interpret dynamics and pattern formation of tissue growth, it is helpful to have a way to simulate the cell systems and test hypotheses about the mechanisms by which the system is responding. Computer “modeling experiments” can serve this purpose, as we show with an example of the small intestine epithelial cells' response to cytosine arabinoside. This example demonstrates that nonhomogeneities in the cell population can play an important role and emphasizes the need to use a modeling approach, like our spatial modeling, that addresses the differences within the tissue structure. These types of “modeling experiments” can guide researchers with further experiments and provide clues as to how complex cell proliferation behavior is linked to underlying molecular phenomena.     

Intelligent systems in biology: why the excitement?

Biology has rapidly become a data-rich, information-hungry science because of recent massive data generation technologies. Our biological colleagues are designing more clever and informative experiments because of recent advances in molecular science. These experiments and data hold the key to the deepest secrets of biology and medicine, but we cannot fully analyze this data due to the wealth and complexity of the information available. The result is a great need for intelligent systems in biology. There are many opportunities for intelligent systems to help produce knowledge in biology and medicine. Intelligent systems probably helped design the last drug your doctor prescribed, and they were probably involved in some aspect of the last medical care you received. Intelligent computational analysis of the human genome will drive medicine for at least the next half-century. Intelligent systems are working on gene expression data to help understand genetic regulation and ultimately the regulated control of all life processes including cancer, regeneration, and aging. Knowledge bases of metabolic pathways and other biological networks make inferences in systems biology that, for example, let a pharmaceutical program target a pathogen pathway that does not exist in humans, resulting in fewer side effects to patients. Modern intelligent analysis of biological sequences produces the most accurate picture of evolution ever achieved. Knowledge-based empirical approaches currently are the most successful method known for general protein structure prediction. Intelligent literature-access systems exploit a knowledge flow exceeding half a million biomedical articles per year. Machine learning systems exploit heterogenous online databases whose exponential growth mimics Moore's law.     

Mining spatiotemporal co-occurrence patterns in non-relational databases

Spatiotemporal co-occurrence patterns (STCOPs) represent the subsets of feature types whose instances are frequently co-occurring both in space and time. Spatiotemporal co-occurrences reflect the spatiotemporal overlap relationships among two or more spatiotemporal instances both in spatial and temporal dimensions. STCOPs can be potentially used to predict and understand the generation and evolution of different types of interacting phenomena in various scientific fields such as astronomy, meteorology, biology, geosciences. Meaningful and statistically significant data analysis for these scientific fields requires processing sufficiently large datasets. Due to the computationally expensive nature of spatiotemporal operations required for mining spatiotemporal co-occurrences, it is increasingly difficult to identify spatiotemporal co-occurrences and discover STCOPs in centralized system settings. As a solution, we developed a cloud-based distributed mining system for discovering STCOPs. Our system uses Accumulo, a column-oriented non-relational database management system as its backbone. In order to efficiently mine the STCOPs, we propose three data models for managing trajectory-based spatiotemporal data in Accumulo. We introduce an in-memory join-index structure and a join algorithm for effectively performing spatiotemporal join operations on spatiotemporal trajectories in non-relational databases. Lastly, with the experiments with artificial and real life datasets, we evaluate the performance of the proposed models for STCOP mining.     

Review of some advances and applications in real-time high-speed vision: Our views and experiences

The frame rate of conventional vision systems is restricted to the video signal formats(e.g., NTSC 30 fps and PAL 25 fps that are designed on the basis of the characteristics of the human eye, which implies that the processing speed of these systems i limited to the recognition speed of the human eye. However, there is a strong demand for real-time high-speed vision sensors in many application fields, such as factory automation, biomedicine, and robotics, where high-speed operations are carried out. Thes high-speed operations can be tracked and inspected by using high-speed vision systems with intelligent sensors that work at hundred of Hertz or more, especially when the operation is difficult to observe with the human eye. This paper reviews advances in developing real-time high speed vision systems and their applications in various fields, such as intelligent logging systems, vibration dynamic sensing, vision-based mechanical control, three-dimensional measurement/automated visual inspection, vision-based human interface and biomedical applications.     

智能机器翻译系统中的语境处理

现有机器翻译系统一般都是集中在句子层面上进行翻译，较少考虑句子之间的语境信息，导致有许多问题不能很好解决．文中基于全句翻译的智能英汉机器翻译系统ＩＭＴ／Ｅ－８６３构造了一个语言环境ＩＭＴＥＮＶ，用来处理组合歧义、省略等语言现象．     

An intelligent agent-based architecture for strategic information system applications

Strategic information systems (SIS) focus on the use of information system (IS) and information technology (IT) in the strategic management process in business organizations. The emphasis is on the strategic view of IS and IT and their impact on organizational strategy. Increased competition and advances in information technologies push for considerable structural changes in SIS. Agents, as autonomous entities which either work on their own or cooperate with others, and agent architectures have enormous potentials to be applied in such critical systems. In this article, first we investigate the very fundamental concepts of strategic information systems and intelligent agent technology. Then, the discussion continues on the specification of the characteristics and implementation issues of a typical SIS. Afterwards, we make use of these concepts and integrate them into a state-of-the-art, intelligent architecture for strategic information systems, called intelligent agent-based SIS. This is a comprehensive framework for a SIS in IT era which may be put into practice by a team of professionals in the near future. The graphical representation of this model is intended to help the reader understand the concept much better. After explaining the suggested model in full details, we introduce some support agents and specify their corresponding roles in an intelligent agent-based SIS architecture. Discussions and concluding remarks regarding the proposed system are provided at the end of the paper.     

MACHINE PROCESSING OF SINHALA NATURAL LANGUAGE: A STEP TOWARD INTELLIGENT SYSTEMS

An intelligent machine can be thought of as a human friendly machine system that identifies or understands the problems of generating tasks, developing plans, compiling and executing the tasks automatically. High performance dependable intelligent systems must understand and translate natural languages. The translation of natural languages for intelligent systems has been one of the most challenging problems in intelligent systems from the very beginning. It is the responsibility of a translation system to assign the responsibility of task generation ability of the machine to automate a program generation.

In this paper, the problem of advanced machine translation capabilities is approached by examining the Sinhala natural language. Sinhalese has not been analyzed using computational linguistics. Our earlier system on Sinhalese morphology is the first attempt of such a study. This paper extends it to syntactic and semantic analysis. We formalize grammar rules for unit, phrase, clause and sentence, and developed a semantically characteristic Sinhalese dictionary, and a conceptual dictionary based on English, Japanese, and Sinhalese. Syntactic and semantic analyses are implemented on the computer and sound experimental results are obtained.     

Integration framework for intelligent manufacturing processes

In this article, we describe a new approach to applying distributed artificial intelligence techniques to manufacturing processes. The construction of intelligent systems is one of the most important techniques among artificial intelligence research. Our goal is to develop an integrated intelligent system for real time manufacturing processes. An integrated intelligent system is a large knowledge integration environment that consists of several symbolic reasoning systems (expert systems) and numerical computation packages. These software programs are controlled by a meta-system which manages the selection, operation and communication of these programs. A meta-system can be implemented in different language environments and applied to many disciplines. This new architecture can serve as a universal configuration to develop high performance intelligent systems for many complicated industrial applications in real world domains.To whom all correspondence should be addressed.     

基于主从异步复制技术的容灾实时系统研究与实现

介绍了一个实用的容灾实时系统SDP_R．为提高系统的性能，SDP_R采用严格的主从异步复制技术和多线程技术．实验表明，多线程技术和多进程技术能够较大地提高系统的性能，而且相比多进程技术，多线程技术所需的内存要少得多．详细探讨了采用多线程技术时怎样保证数据的一致性．SDP_R系统不仅满足容灾系统设计准则，而且与Oracle以及Informix等数据库提供商提供的数据复制技术相比，SDP_R通过数据库底层函数层可以屏蔽不同数据库产品或版本之间的差异，从而实现不同数据库产品和版本之间的数据复制．SDP_R适用于使用多种数据库产品或版本的系统，如智能网系统．同时，SDP_R系统可以处理数据拷贝图为任意拓扑结构的情况．容灾系统SDP_R已经实现，它是下一代智能网分布式SDP中的一个主要功能模块．     

系统生物学的分析与建模

要从系统水平上理解生物系统，必需建立一个新的框架．这个新的框架就是系统生物学，其目的在于在系统水平上阐述和理解生物系统．本文试图以系统控制学科的视点，评述系统生物学的分析与建模的主要思路和方法，以及系统生物学发展现状和可能的发展前景．     

NOVELTY DETECTION AND IN-LINE LEARNING OF NOVEL CONCEPTS ACCORDING TO A CASE-BASED REASONING PROCESS SCHEMA FOR HIGH-CONTENT IMAGE ANALYSIS IN SYSTEM BIOLOGY AND MEDICINE

Novelty detection, the ability to identify new or unknown situations that were never experienced before, is useful for intelligent systems aspiring to operate in environments where data are acquired incrementally. This characteristic is common to numerous problems in medical diagnosis and visual perception. We propose to see novelty detection as a case-based reasoning (CBR) process. Our novelty-detection method is able to detect the novel situation as well as to use the novel events for immediate reasoning. To ensure this capacity we combine statistical and similarity inference and learning. This view of CBR takes into account the properties of data such as the uncertainty and the underlying concepts such as storage, learning, retrieval and indexing can be formalized and performed efficiently. The novel events can be items being outliers or items making up a new class. Storage is done in a structured fashion, so that they can give a strategy for controlling the statistical learning process. Only when enough samples are available for a group of novel events, the statistical learning process proves if new models should be learned or if old models should be reorganized according to the new data. We use our novelty detection and handling method in a scenario where cell images may vary according to the particular cell line and the varying image quality given by the image acquisition unit. This scenario is valid for many medical applications and applications in system biology.