International Journal of Cooperative Information Systems

In this paper, we present, formalize and prove the correctness of recovery algorithm for our open and safe nested transaction model using I/O automaton model. Our nested transaction model uses the notion of a recovery point subtransaction in the nested transaction tree. It introduces a prewrite operation before each write operation to increase the potential concurrency. Our transaction model is termed as "open and safe" as prewrites allow early reads (before database writes on disk) without cascading aborts. The systems restart and buffer management operations are modelled as nested transactions to exploit possible concurrency during restart. Each non-access transaction, object, and the scheduler is modeled as I/O automaton. Each of these automata is specified with the help of some pre-and post-conditions. These pre-and post-conditions capture the operational semantics and the behavior of each automaton during recovery operations. Our proof technique makes use of assertional reasoning and provide many interesting invariant, thus gives a better understanding of our recovery algorithm.

Agent-based information filtering alleviates the problem of information overload on the Internet by proactively scanning through the incoming stream of information on behalf of the users. Nevertheless, users' information needs will change over time. Therefore, it is essential for the information filtering agents to learn and adapt to the users' changing information needs in order to maintain the accuracy of the filtering process. Applying logic-based representation and adaptation to adaptive information filtering agents is promising since the semantic relationships among information items can be captured and reasoned about during the agents' learning and adaptation processes. This opens the door to a more responsive reinforcement learning than can be obtained from a purely statistical approach. The AGM belief revision paradigm that models rational and minimal change of an agent's beliefs offers a sound theoretical foundation for constructing the learning components of adaptive information filtering agents. This paper describes a symbolic framework for representing domain knowledge in an adaptive information filtering agent, and illustrates how the AGM belief revision paradigm can be applied to develop the agent's learning mechanism.

Recommender Systems (RS), allow users to share information about items they like or dislike and obtain, in a timely fashion, recommendations based on predictions about unseen items (physical or information goods and/or services). In this process, users' preferences are considered to be the learning target functions. We study Agent-based Recommender Systems (ARS) under the scope of online learning in Multi-Agent systems (MAS). This approach models the problem as a pool of independent cooperative predictor agents, one per each user (the masters) in the system, in situations in which each agent (the learners) faces a sequence of trials, with a prediction to make in every step, eventually getting the correct value from its master. Each learner is willing to discover the degree of similarity among the target function of its master and those of other agents' masters (i.e. preference similarity). The agent uses this information for the calculation of its own prediction task, the goal being to make as few mistakes as possible. A simple, yet effective method is introduced in order to construct a compound algorithm for each agent by combining memory-based individual prediction and online weighted-majority voting. We give a theoretical mistake bound for this algorithm that is closely related to the total loss of the best predictor agent in the pool. Finally, we conduct some experiments obtaining results that empirically support these ideas and theories.

In this paper, we propose a system for representing interaction protocols called POS which is both Turing complete and determine a complete semantics of protocols. This work is inspired by the Structured Operational Semantics in programming languages. We precisely define POS and illustrate its power on an extended example.

The exchange of goods and services between bargaining software agents requires new forms of brokering mechanisms which achieve consensus between conflicting parties. Such mechanisms have to be designed in a way that they give rational self-interested agents no incentives for insincere behavior. We introduce an arbiter as third party that resolves conflicting bargaining situations between the agents. To achieve non-manipulative agent behavior, we investigate three arbitration protocols that avoid different forms of manipulations and show how each trades net efficiency for robustness against manipulations. We describe the applicability of the protocols in bilateral bargaining situations and, analyze their robustness against manipulations analytically and by simulations. We compare the protocols with Nash's arbitration¹ and the Groves-Clarke tax² and characterize situations in which our protocols are superior.

The Web is based on a browsing paradigm that makes it difficult to retrieve and integrate data from multiple sites. Today, the only way to do this is to build specialized applications, which are time-consuming to develop and difficult to maintain. We have addressed this problem by creating the technology and tools for rapidly constructing information agents that extract, query, and integrate data from web sources. Our approach is based on a uniform representation that makes it simple and efficient to integrate multiple sources. Instead of building specialized algorithms for handling web sources, we have developed methods for mapping web sources into this uniform representation. This approach builds on work from knowledge representation, databases, machine learning and automated planning. The resulting system, called Ariadne, makes it fast and easy to build new information agents that access existing web sources. Ariadne also makes it easy to maintain these agents and incorporate new sources as they become available.

Knowledge fusion refers to the process of locating and extracting knowledge from multiple, heterogeneous on-line sources, and transforming it so that the union of the knowledge can be applied in problem-solving. The KRAFT project has defined a generic agent-based architecture to support fusion of knowledge in the form of constraints expressed against an object data model. KRAFT employs three kinds of agent: facilitators locate appropriate on-line sources of knowledge; wrappers transform heterogeneous knowledge to a homogeneous constraint interchange format; mediators fuse the constraints together with associated data to form a dynamically-composed constraint satisfaction problem, which is then passed to an existing constraint solver engine to compute solutions. The paper presents the KRAFT architecture and the three kinds of agent, and includes a description of a demonstration KRAFT application in the domain of telecommunications service provision.

A digital city is a social information infrastructure for urban life (including shopping, business, transportation, education, welfare and so on). We started a project to develop a digital city for Kyoto based on the newest technologies including cooperative information agents. This paper presents an architecture for digital cities and shows the roles of agent interfaces in it. We propose two types of cooperative information agents as follows: (a) the front-end agents determine and refine users' uncertain goals, (b) the back-end agents extract and organize relevant information from the Internet, (c) Both types of agents opportunistically cooperate through a blackboard. We also show the research guidelines towards social agents in digital cities; the agent will foster social interaction among people who are living in/visiting the city.

Surprising findings showing that many basic rules of social interaction among humans also apply to the interaction of humans with computers²⁶ have encouraged interface researchers to proclaim a new paradigm in human computer interface design: the social interface. Reeves and Nass' findings have especially given a new impetus to research on anthropomorphic agents, suggesting that furnishing an agent with a human-like interface could be an effective way to enhance agent usability. Little research exists however, to verify this claim. This study seeks to illuminate user responses to an anthropomorphic agent if it is used on a daily basis. It examines the influence of traditional usability variables such as control, understanding, trust, and distraction on the acceptance of an anthropomorphic interface. Kaasinen⁸ as well as Wexelblat and Maes³⁶ emphasize that the acceptance of agents (whether or not they have an anthropomorphic interface) strongly depends on these traditional usability variables.

As subject for this study, a commonly used anthropomorphic agent, the Microsoft Office Assistant, was selected. Using a questionnaire that was distributed through email and the World Wide Web as well as on paper, 105 users of the Microsoft Office Suite were asked about their use and acceptance of the Office Assistant. It turned out that the Office Assistant feature was rejected by the majority of the users. As a feature to troubleshoot problems, it was perceived as inefficient. Also, from a "social" perspective, it was rated as not particularly engaging or motivating. Multiple regression analysis revealed that variables influencing the use and the acceptance of the Office Assistant were distraction and trust. Only those users who felt distracted by the assistant and who did not trust in its capability to solve their problems rejected the Office Assistant. It is concluded that to ensure agent usability, its design should emphasize assistance with the user's task over social mimicry. If design efforts to furnish the agent with social traits interfere with the user's principal goal, it is likely that the agent will be rejected.