“How do you know that I don’t understand?” A look at the future of intelligent tutoring systems

Many software systems would significantly improve performance if they could adapt to the emotional state of the user, for example if Intelligent Tutoring Systems (ITSs), ATM’s, ticketing machines could recognise when users were confused, frustrated or angry they could guide the user back to remedial help systems so improving the service. Many researchers now feel strongly that ITSs would be significantly enhanced if computers could adapt to the emotions of students. This idea has spawned the developing field of affective tutoring systems (ATSs): ATSs are ITSs that are able to adapt to the affective state of students. The term “affective tutoring system” can be traced back as far as Rosalind Picard’s book Affective Computing in 1997.This paper presents research leading to the development of Easy with Eve, an ATS for primary school mathematics. The system utilises a network of computer systems, mainly embedded devices to detect student emotion and other significant bio-signals. It will then adapt to students and displays emotion via a lifelike agent called Eve. Eve’s tutoring adaptations are guided by a case-based method for adapting to student states; this method uses data that was generated by an observational study of human tutors. This paper presents the observational study, the case-based method, the ATS itself and its implementation on a distributed computer systems for real-time performance, and finally the implications of the findings for Human Computer Interaction in general and e-learning in particular. Web-based applications of the technology developed in this research are discussed throughout the paper.     

Theory and implementation of path planning by negotiation for decentralized agents

This paper presents a cooperative decentralized path-planning algorithm for a group of autonomous agents that provides guaranteed collision-free trajectories in real-time. The algorithm is robust with respect to arbitrary delays in the wireless traffic, possible sources being transmission time and error correction. Agents move on reserved areas which are guaranteed not to intersect, therefore ensuring safety. A handshaking procedure guarantees recent information states for the agents. Conflicts between agents are resolved by a cost-based negotiation process. The basic algorithm is augmented by the introduction of waypoints, which increase performance at the cost of additional wireless traffic. An implementation of the algorithm is tested in simulation and successfully applied to a real system of autonomous robots. The results are presented and discussed.     

Top-down vs bottom-up methodologies in multi-agent system design

Traditionally, two alternative design approaches have been available to engineers: top-down and bottom-up. In the top-down approach, the design process starts with specifying the global system state and assuming that each component has global knowledge of the system, as in a centralized approach. The solution is then decentralized by replacing global knowledge with communication. In the bottom-up approach, on the other hand, the design starts with specifying requirements and capabilities of individual components, and the global behavior is said to emerge out of interactions among constituent components and between components and the environment. In this paper we present a comparative study of both approaches with particular emphasis on applications to multi-agent system engineering and robotics. We outline the generic characteristics of both approaches from the MAS perspective, and identify three elements that we believe should serve as criteria for how and when to apply either of the approaches. We demonstrate our analysis on a specific example of load balancing problem in robotics. We also show that under certain assumptions on the communication and the external environment, both bottom-up and top-down methodologies produce very similar solutions.

A dynamic workflow framework for mass customization using web service and autonomous agent techniques

Custom software development and maintenance is one of the key expenses associated with developing automated systems for mass customization. This paper presents a method for reducing the risk associated with this expense by developing a flexible environment for determining and executing dynamic workflow paths. Strategies for developing an autonomous agent-based framework and for identifying and creating web services for specific process tasks are presented. The proposed methods are outlined in two different case studies to illustrate the approach for both a generic process with complex workflow paths and a more specific sequential engineering process.     

Controllability of multi-agent systems based on agreement protocols

This paper investigates the controllability of multi-agent systems based on agreement protocols. First, for a group of single-integrator agents, the controllability is studied in a unified framework for both networks with leader-following structure and networks with undirected graph. Some new necessary/sufficient conditions for the controllability of networks of single-integrator agents are established. Second, we prove that, under the same topology and same prescribed leaders, a network of high-order dynamic agents is completely controllable if and only if so is a network of single-integrator agents. Third, how the selection of leaders and the coupling weights of graphs affect the controllability is analyzed. Finally, some numerical simulations are presented to demonstrate the effectiveness of the theoretical results.     

基于代理的工作流执行服务的探讨

工作流理论不断发展,工作流管理系统也逐渐成为目前研究的热点之一.而工作流执行系统作为工作流管理系统的核心部件,其工作效率也越来越引起人们的关注.通过利用代理技术,提出了一种新的工作流执行服务架构,并对工作流引擎的执行过程进行了分析.     

Toward a programming theory for rational agents

An important problem in agent verification is a lack of proper understanding of the relation between agent programs on the one hand and agent logics on the other. Understanding this relation would help to establish that an agent programming language is both conceptually well-founded and well-behaved, as well as yield a way to reason about agent programs by means of agent logics. As a step toward bridging this gap, we study several issues that need to be resolved in order to establish a precise mathematical relation between a modal agent logic and an agent programming language specified by means of an operational semantics. In this paper, we present an agent programming theory that provides both an agent programming language as well as a corresponding agent verification logic to verify agent programs. The theory is developed in stages to show, first, how a modal semantics can be grounded in a state-based semantics, and, second, how denotational semantics can be used to define the mathematical relation connecting the logic and agent programming language. Additionally, it is shown how to integrate declarative goals and add precompiled plans to the programming theory. In particular, we discuss the use of the concept of higher-order goals in our theory. Other issues such as a complete axiomatization and the complexity of decision procedures for the verification logic are not the focus of this paper and remain for future investigation. Part of this research was carried out while the first author was affiliated with the Nijmegen Institute for Cognition and Information, Radboud University Nijmegen.     

A learning classifier system for mazes with aliasing clones

Maze problems represent a simplified virtual model of the real environment and can be used for developing core algorithms of many real-world application related to the problem of navigation. Learning Classifier Systems (LCS) are the most widely used class of algorithms for reinforcement learning in mazes. However, LCSs best achievements in maze problems are still mostly bounded to non-aliasing environments, while LCS complexity seems to obstruct a proper analysis of the reasons for failure. Moreover, there is a lack of knowledge of what makes a maze problem hard to solve by a learning agent. To overcome this restriction we try to improve our understanding of the nature and structure of maze environments. In this paper we describe a new LCS agent that has a simpler and more transparent performance mechanism. We use the structure of a predictive LCS model, strip out the evolutionary mechanism, simplify the reinforcement learning procedure and equip the agent with the ability to Associative Perception, adopted from psychology. We then assess the new LCS with Associative Perception on an extensive set of mazes and analyse the results to discover which features of the environments play the most significant role in the learning process. We identify a particularly hard feature for learning in mazes, aliasing clones, which arise when groups of aliasing cells occur in similar patterns in different parts of the maze. We discuss the impact of aliasing clones and other types of aliasing on learning algorithms.     

Shopping System Model in Electronic Commerce

Despite its name, e-commerce is not fully automated. Typically, online transactions require a significant level of human intervention. Customers undertake the search for products, services, potential vendors and business partners. They evaluate alternatives, decide what goods to buy and when, from which vendor, and how much they are willing to pay for. They engage in potential negotiations, carry out the transactions and so on. As those markets become mature, new products and services, as well as new practi...     

Synthesis of Branched DNA Scaffolded Super‐Nanoclusters with Enhanced Antibacterial Performance

Metal nanoclusters (NCs) possess unique optical properties, and exhibit a wide variety of potential applications. DNA with robust molecular programmability is demonstrated as an ideal scaffold to regulate the formation of NCs, offering a rational approach to precisely tune the spatial structures of NCs. Herein, the first use of branched DNA as scaffold to regulate the formation of silver nanoclusters (super‐AgNC) is reported, in which the spatial structures are precisely designed and constructed. Super‐AgNC with tunable shapes and arm‐lengths including Y‐, X‐, and (Y–X)‐ shaped super‐AgNC is achieved. The molecular structures and optical properties of super‐AgNCs are systemically studied. As a proof of application, remarkably, super‐AgNCs exhibit superior antibacterial performance. In addition, super‐AgNCs show excellent biocompatibility with three types of tissue cells including 293T (human embryonic kidney cells), SMCs (vascular smooth muscle cells), and GLC‐82 (lung adenocarcinoma cells). These performances enable the super‐AgNCs adaptable in a variety of applications such as biosensing, bioimaging, and antibacterial agents.