A formal specification of dMARS

d’Inverno, Mark; Kinny, David; Lück, Michael; Wooldridge, Michael

doi:10.1007/bfb0026757

Cited by 195 publications

(102 citation statements)

References 10 publications

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“…This work addresses the more detailed aspects involved in dealing with the transition of a fully designed system, rather than an outline structure. Second, we allow an easy and simple comparison of 3APL and its competitor systems that have been specified in a similar style such as AgentSpeak(L) [3] and dMARS [2], as illustrated in [6]. Third, we provide an accessible resource in the specification of techniques for the development of agent systems that might not otherwise be available in a form relevant both to agent architects and developers.…”

Section: Discussionmentioning

confidence: 99%

A Formal Architecture for the 3APL Agent Programming Language

d’Inverno

Hindriks

Lück

2000

ZB 2000: Formal Specification and Development in Z and B

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Abstract. The notion of agents has provided a way of imbuing traditional computing systems with an extra degree of flexibility that allows them to be more resilient and robust in the face of more varied and unpredictable forms of interaction. One class of agents, typically called intelligent agents, represent their world symbolically according to their beliefs, have goals which need to be achieved, and adopt plans or intentions to achieve them. Now, one approach to building agents is to design a programming language whose semantics are based on some theory of rational or intentional agency and to program the desired behaviour of individual agents directly using mental attitudes. Such a technique is referred to as agent oriented programming. Arguably, the most innovative of these languages is 3APL (pronounced "triple-a-p-l") which supports the construction of intelligent agents for the development of complex systems through a set of intuitive concepts like beliefs, goals and plans. In this paper, we provide a Z specification of the programming language 3APL which provides a basis for implementation and also adds to a growing library of agent techniques and features.

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Section: Discussionmentioning

confidence: 99%

A Formal Architecture for the 3APL Agent Programming Language

d’Inverno

Hindriks

Lück

2000

ZB 2000: Formal Specification and Development in Z and B

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“…Although BDI and PRS are an effective way of agent representing reasoning, there was no formal definition of how the abstract concepts should be defined within an agent, meaning there have been many different variations in implementing this approach (D'Inverno et al, 1998). One approach to implementing BDI theory within an agent is called agent-oriented programming (Shoham, 1993).…”

Section: Theorymentioning

confidence: 99%

On the multi-agent stochastic simulation of occupants in buildings

Chapman

Siebers

Robinson

2018

Journal of Building Performance Simulation

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One of the principle causes for deviations between predicted and simulated performance of buildings relates to the stochastic nature of their occupants: their presence, activities whilst present, activity dependent behaviours and the consequent implications for their perceived comfort. A growing research community is active in the development and validation of stochastic models addressing these issues; and considerable progress has been made. Specifically models in the areas of presence, activities while present, shading devices, window openings and lighting usage.One key outstanding challenge relates to the integration of these prototype models with building simulation in a coherent and generalizable way; meaning that emerging models can be integrated with a range of building simulation software. This thesis describes our proof of concept platform that integrates stochastic occupancy models within a multi agent simulation platform, which communicates directly with building simulation software. The tool is called Nottingham Multi-Agent Stochastic Simulation (No-MASS).No-MASS is tested with a building performance simulation solver to demonstrate the effectiveness of the integrated stochastic models on a residential building and a non-residential building. To account for diversity between occupants No-MASS makes use of archetypical behaviours within the stochastic models of windows, shades and activities. Thus providing designers with means to evaluate the performance of their designs in response to the range of expected behaviours and to evaluate the robustness of their design solutions; which is not possible using current simplistic deterministic representations. No-MASS employs agent machine learning techniques that allow them to learn how to respond to the processes taking place within a building and agents can choose a strategy without the need for context specific rules.Employing these complementary techniques to support the comprehensive simulation of occupants presence and behaviour, integrated within a single platform that can readily interface with a range of building (and urban) energy simulation programs is the key contribution to knowledge from this thesis. Nevertheless, there is significant scope to extend this work to further reduce the performance gap between simulated and real world buildings.

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“…Further references to BDI model are based on dMARS [6]. A BDI agent includes a queue of events which stores internal and external events occurring in the system, some beliefs, a library of plans, a stack of desires and a stack of intentions (instantiated plans to reach the desires).…”

Section: The Implementationmentioning

confidence: 99%

Simulating a Human Cooperative Problem Solving

Pauchet¹,

Seghrouchni²,

Chaignaud³

2007

Multi-Agent Systems and Applications V

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Abstract. We are interested in understanding and simulating how humans elaborate plans in situations where knowledge is incomplete and how they interact to obtain missing information. Our human interaction model is based on the speech act theory to model the utterances and it uses timed automata to describe the dynamics of the dialogs. Our human planning model is implemented as a hierarchical blackboard architecture which manages opportunistic planning. The system BDIggy we propose, is a concurrent implementation of the planning model and the interaction model through the BDI concept. This system is used to simulate the human processes during cooperative problem solving.

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A formal specification of dMARS

Cited by 195 publications

References 10 publications

A Formal Architecture for the 3APL Agent Programming Language

A Formal Architecture for the 3APL Agent Programming Language

On the multi-agent stochastic simulation of occupants in buildings

Simulating a Human Cooperative Problem Solving

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