Intelligent-agent technology is one of the most exciting, active areas of research and development in computer science and information technology today. The First Asia-Pacific Conference on Intelligent- Agent Technology (IAT'99) attracted researchers and practitioners from diverse fields such as computer science, information systems, business, telecommunications, manufacturing, human factors, psychology, education, and robotics to examine the design principles and performance characteristics of various approaches in agent technologies and, hence, fostered the cross-fertilization of ideas on the development of autonomous agents and multiagent systems among different domains.

Fraud causes substantial losses to telecommunication carriers. Detection systems which automatically detect illegal use of the network can be used to alleviate the problem. Previous approaches worked on features derived from the call patterns of individual users. In this paper we present a call-based detection system based on a hierarchical regime-switching model. The detection problem is formulated as an inference problem on the regime probabilities.

Fraud causes substantial losses to telecommunication carriers.

This paper examines the application of reinforcement learning to a telecommunications networking problem. The problem requires that revenue bemaximized while simultaneously meeting a quality of service constraint that forbids entry into certain states. We present a general solution to this multi-criteria problem that is able to earn significantly higher revenues than alternatives.

This paper examines the application of reinforcement learning to a telecommunications networking problem. The problem requires that revenue be maximized while simultaneously meeting a quality of service constraint that forbids entry into certain states. We present a general solution to this multi-criteria problem that is able to earn significantly higher revenues than alternatives.

Peter Dayan E25-210, MIT Cambridge, MA 02139 We provide a model of the standard watermaze task, and of a more challenging task involving novel platform locations, in which rats exhibit one-trial learning after a few days of training. The model uses hippocampal place cells to support reinforcement learning, and also, in an integrated manner, to build and use allocentric coordinates. 1 INTRODUCTION

We provide a model of the standard watermaze task, and of a more challenging task involving novel platform locations, in which rats exhibit one-trial learning after a few days of training. The model uses hippocampal place cells to support reinforcement learning, and also, in an integrated manner, to build and use allocentric coordinates. 1 INTRODUCTION

We provide a model of the standard watermaze task, and of a more challenging task involving novel platform locations, in which rats exhibit one-trial learning after a few days of training. The model uses hippocampal place cells to support reinforcement learning, and also, in an integrated manner, to build and use allocentric coordinates. 1 INTRODUCTION

This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems. AntNet's agents concurrently explore the network and exchange collected information. The communication among the agents is indirect and asynchronous, mediated by the network itself. This form of communication is typical of social insects and is called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming from the telecommunications and machine learning fields. The algorithms' performance is evaluated over a set of realistic testbeds. We run many experiments over real and artificial IP datagram networks with increasing number of nodes and under several paradigmatic spatial and temporal traffic distributions. Results are very encouraging. AntNet showed superior performance under all the experimental conditions with respect to its competitors. We analyze the main characteristics of the algorithm and try to explain the reasons for its superiority.

We applied mobile computing to community support and explored mobile computing with a large number of terminals. This article reports on the Second International Conference on Multiagent Systems (ICMAS'96) Mobile Assistant Project that was conducted at an actual international conference for multiagent systems using 100 personal digital assistants (PDAs) and cellular telephones. We supported three types of service: (1) communication services such as e-mail and net news; (2) information services such as conference, personal, and tourist information; and (3) community support services such as forum and meeting arrangements. After the conference, we analyzed a large amount of log data and obtained the following results: It appears that people continuously used PDAs in their hotel rooms after dinner; e-mail services were used independently of the conference structure, but the load on information services reflected the schedule of the conference. Postquestionnaire data showed that our trial was considered interesting, although people were not fully satisfied with the PDAs and services provided. Participants showed a deep interest in mobile computing for community support.