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.     

The shadow approach: An orphan detection protocol for mobile agents

Orphan detection in distributed systems is a well-researched field for which many solutions exist. These solutions exploit well defined parent-child relationships given in distributed systems. But they are not applicable in mobile agent systems, since no similar natural relationship between agents exist. Thus new protocols have to be developed. In this paper one such protocol for controlling mobile mobile agents and for orphan detection is presented. The shadow approach presented in this paper uses the idea of a placeholder (shadow) which is assigned by the agent system to each new agent. This defines an artificial relationship between agents and shadow. The shadow records the location of all dependent agents. Removing the root shadow implies that all dependent agents are declared orphan and are eventually be terminated. We introduce agent proxies that create a path from shadow to every agent. In an extension of the basic protocol we additionally allow the shadow to be mobile.The shadow approach can be used for termination of groups of agents even if the exact location of each single agent is not known.     

呋喃树脂低温固化性能测试

采用一种新合成的０３＃固化剂,可以使呋喃树脂在常温下缩短固化时间,并且在低温下可以固化．测试结果表明：在低温下呋喃树脂的固化性能、粘结强度和体积安定性均符合技术要求．     

Normalized Interactions between autonomous agents

The CEC Project GOAL (Esprit 6283) aims to develop generic software tools to support a new project management paradigm, in which projects are collaborative, decentralised and inter-organizational. To support inter-organizational interaction, communication and cooperation, we are developing a design framework for formalizing the flow of information between organizations, specifying access to and provision of project services, and defining project-wide standards and procedures. This framework is based on normalizing interactions between autonomous software agents by specifying messages and protocols for inter-agent communication and cooperation. This paper reviews the framework, and then focusses on the specification and implementation of a case study, the automation of a distributed document review procedure. This is both a successful proof of concept and a demonstration of how Artificial Intelligence technologies can support inter-organizational project management. It also points the way to agent brokering, an enhancement of object brokering in distributed open systems, where the satisfaction of service requests can be subject to negotiation.Supported by CEC Esprit Project GOAL (Esprit 6283) and CEC Esprit BRA Medlar II (Esprit 6471).Supported by CEC Esprit Project GOAL (Esprit 6283).     

Social evaluations of embodied agents and avatars

The purpose of this study was to examine social evaluations (i.e., perceptions of empathy and positivity) following peoples’ interactions with digital human representations. Female research participants engaged in a 3-min interaction while immersed in a 3-D immersive virtual environment with a “peer counselor.” Participants were led to believe that the peer counselor was either an embodied agent (i.e., computer algorithm) or an avatar (i.e., another person). During the interaction, the peer counselor either smiled or not. As predicted, a digitally-rendered smile was found to affect participants’ social evaluations. However, these effects were moderated by participants’ beliefs about their interaction partner. Specifically, smiles enhanced social evaluations of embodied agents but degraded them for avatars. Although these results are consistent with other findings concerning the communicative realism of embodied agents and avatars they uniquely demonstrate that people’s beliefs alone, rather than actual differences in virtual representations, can impact social evaluations.