Song Shen

Decision-making of BDI agents, a fuzzy approach

This paper introduces and proposes agent fuzzy decision-making (AFDM), as an extension to the classical belief-desire-intention (BDI) model. AFDM addresses the limitations of present formalisms within BDI models by making decisions based on quantified fuzzy judgment. The AFDM matrix model enables quantitative calculation and thus, provides a more practical solution to BDI models. In addition, more flexible and controllable solutions to BDI persistence can be expected with the introduction of AFDM.

Wireless sensor networks, an energy-aware and utility-based BDI agent approach

Large-scale wireless sensor applications require that the Wireless Sensor Network (WSN) be sufficiently intelligent to make decisions or tradeoffs within a partially uncertain environment. Such intelligence is difficult to model because the sensor nodes are resource-bounded, especially on energy supply. Within this paper, we model a WSN as a two-level multiagent architecture and introduce the concept of energy-aware and utility-based Belief, Desire, Intention (BDI) agents. We subsequently formalise the fuzzy-set-based belief-generating and theoretically deduce the rules of the meta-level reasoning of a WSN. We further apply a practical reasoning algorithm which enables cost awareness and status awareness within a WSN. A Tileworld-based simulation and subsequent comparison of the effectiveness of bold, cautious and energy-aware and utility-based agents demonstrates that an energy-aware and utility-based BDI agent outperforms other traditional agents, especially when the environment is highly dynamic.

Fuzzy-set-based decision making through energy-aware and utility agents within wireless sensor networks

Multi-agent systems (MAS) through their intrinsically distributed nature offer a promising software modelling and implementation framework for wireless sensor network (WSN) applications. WSNs are characterised by limited resources from a computational and energy perspective; in addition, the integrity of the WSN coverage area may be compromised over the duration of the network’s operational lifetime, as environmental effects amongst others take their toll. Thus a significant problem arises—how can an agent construct an accurate model of the prevailing situation in order that it can make effective decisions about future courses of action within these constraints? In this paper, one popular agent architecture, the BDI architecture, is examined from this perspective. In particular, the fundamental issue of belief generation within WSN constraints using classical reasoning augmented with a fuzzy component in a hybrid fashion is explored in terms of energy-awareness and utility.

AToM: atomic topology management of wireless sensor networks

This paper explores the structural and behavioural similarities that exist between the chemical constitution of natural substances and WSN (Wireless Sensor Network) topology. It introduces Atomic Topology Management (AToM), which uses concepts like WSN electron, WSN nucleus, WSN photon, and WSN atom to model sensor node, base station, message and basic WSN subset consisting of one base station and several sensor nodes. We subsequently extend the modeling to explain the basic behaviours of WSNs. The paper describes naming rules for topology management and based upon this, we devise and test some basic algorithms for energy and load balancing.

Simulating resource-bounded intelligence for wireless sensor networks

Many embedded devices are characterized by their resource-boundedness. Wireless Sensor Networks (WSNs) are a topical case in point, with energy being the dominant constraint. The issue of the intelligent utilization of energy in sensor nodes is of crucial importance as well as being a formidable software engineering challenge in its own right. Evaluation of an arbitrary intelligence mechanism is difficult as it involves various environmental uncertainties thereby making its effectiveness difficult to assess. Within this paper, Sensorworld is harnessed as a platform for the evaluation and comparison of resource-bounded intelligence. A suite of simulations on effectiveness, utility and energy consumption within the context of dynamism and reasoning strategy are presented. These demonstrate that the validation and comparison of different reasoning strategies is a viable and attainable objective within computationally resource-constrained scenarios.

Agent-based online VAT assessment

Within this paper, we describe an agent-based and customized online VAT assessment solution, together with the data derivation through interaction among software agents. Mathematical modeling of relevant indexes and the notation of threshold are introduced.