Kwok Ching Tsui

Toward nature-inspired computing

NIC-based systems utilize autonomous entities that self-organize to achieve the goals of systems modeling and problem solving.

Autonomy-oriented computing (AOC): formulating computational systems with autonomous components

Autonomous multientity systems are plentiful in natural and artificial worlds. Many systems have been studied in depth and some models of them have been built as computational systems for problem solving. Central to these computational systems is the notion of autonomy. This article surveys research work done along this direction and presents autonomy-oriented computing (AOC) as a paradigm to describe systems for solving hard computational problems and for characterizing the behaviors of a complex system. AOC differs from major complex-system-related studies such as artificial life, simulated evolution, and multiagent systems in that AOC is not just intended to replicate complex behavior, emulate evolution, or coordinate the functioning of many interacting agents. AOC emphasizes the modeling of autonomy in the entities of a complex system and the self-organization of them in achieving a specific goal. Through implemented applications, we describe three main approaches to AOC, as well as an AOC framework with formal definitions of essential constructs and their interrelationships, including the notions of emergent autonomy, self-organization, and the interactions among entities and environment.

Multiagent diffusion and distributed optimization

Distributed problem solving by a multiagent system represents a promising approach to solving complex computational problems. However, many multiagent systems require certain degree of planning, coordination and negotiation to achieve the given goal. This paper presents a multiagent framework for tackling global optimization tasks inspired by diffusion in nature. The framework is designed for situations where agent communication must be kept to a minimal. Hence, complicated coordination and negotiation is not possible. Distributed agents in this framework share the common goal of finding the global optimal solution. They cooperate to achieve this common goal by sharing and updating a common belief that captures their estimation of the whereabouts of the optimal solution. To facilitate this, agents are naturally organized in families with a parent and its offsprings as members. This paper also presents an algorithm called Evolutionary Diffusion Optimization, which is implemented base on the proposed agent framework. Experimental results on some benchmark problems are presented together with performance comparison with a simulated annealing algorithm.

The Intelligent Assistant: An Overview

The Intelligent Assistant (IA) is an integrated system of intelligent software agents that helps the user with communication, information and time management. The IA includes specialist assistants for e-mail prioritization and telephone call filtering (communication management), Web search and Yellow Pages® lookup (information management), and calendar scheduling (time management). Each such assistant is designed to have a model of the user and a learning module for acquiring user preferences. In addition, the IA includes a toolbar providing a graphical interface to the system, a multimodal interface for accepting spoken commands and tracking the user’s activity, and a co-ordinator responsible for managing communication from the system to the user and for initiating system activities on the user’s behalf. A primary design objective of the IA is that its operation is as transparent as possible, to enable the user to control the system as far as is practicable without incurring a heavy overhead when creating and modifying the system’s behaviour. Hence each specialist assistant is designed to represent its user model in a way that is intuitively understandable to non-technical users, and is configured to adaptively modify its user model through time to accommodate the user’s changing preferences. However, in contrast to adaptive interface agents built under the behaviour-based paradigm, the assistants in the IA embrace complex AI representations and machine learning techniques to accomplish more sophisticated behaviour.

Self-organized autonomous web proxies

With the increasing size of the Internet, proxy servers have emerged as a feasible way to reduce the overall network load and latency. More recently researchers focused on new ways to combine multiple cooperative proxies into one transparent proxy system to further increase the overall performance gain, but no work so far was really able to propose an ideal trade-off between content dissemination and clustering in a changing environment caching environment. This paper introduces a self-organizing approach to combine multiple autonomous proxies into one transparent proxy system. One of the emerging attributes of a system of self-organizing autonomous proxies is a balance between content clustering and data dissemination. Our experimental results show that such a system outperforms conventional cooperative proxy infrastructures.

Adaptive distributed caching

This paper introduces an adaptive algorithm for distributed caching based on the idea of autonomous proxy caches without the usage of a central coordinator or broadcasting protocol. We show that the algorithm outperforms existing approaches based on hashing algorithms in hot-spot scenarios and common power-law request patterns.

Autonomy-Oriented Computing (AOC)

This article describes a new programming paradigm called autonomy-oriented computing (AOC), which describes the construct of synthetic autonomy in locally interacting entities, and use the aggregated effects of entity interactions to generate desired global solutions or systems dynamics. The fundamental working mechanism of self-organization that underlies the AOC paradigm offers the advantages of natural formulation as well as scalable performance to characterize complex systems or to computationally hard problems that are distributed and large scale in nature. Keywords: autonomy-oriented computing (AOC); synthetic autonomy; emergent autonomy; self-organization; antonomous entities; multi-agent systems

An Introduction to Soft Computing - A Tool for Building Intelligent Systems

“The essence of soft computing is that unlike the traditional, hard computing, soft computing is aimed at an accommodation with the pervasive imprecision of the real world. Thus, the guiding principle of soft computing is: ‘...exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness, low solution cost and better rapport with reality’. In the final analysis, the role model for soft computing is the human mind.” [1]

Autonomy Oriented Load Balancing in Proxy Cache Servers

Proxy cache servers are used to deal with the increasing demand for information on the Internet by caching the frequently referenced web objects. It is common to have more than one proxy cache servers being installed in one local network. The problem of load balancing then arises as organizations want to utilise the resources in the best way. This article proposes two methods to tackle the load balancing problem. The two methods are based on the notion of autonomy oriented computation where entities in the model are allowed to make local decisions and they only need to interact with local neighbors.

Self-organized load balancing in proxy servers: algorithms and performance

Proxy servers are common solutions to relieve organizational networks from heavy traffic by storing the most frequently referenced web objects in their local cache. These proxies are commonly known as cooperative proxy systems and are usually organized in such a way as to optimize the utilization of their storage capacity. However, the design of the organizational structure of such proxy system depends heavily on the designers knowledge of the networks performance. This article describes three methods to tackle this load balancing problem. They allow the self-organization of proxy servers by modeling each server as an autonomous entity that can make local decisions based on the traffic pattern it has served.