Procedural knowledge

Georgeff, Michael P.; Lansky, Amy

doi:10.1109/proc.1986.13639

Cited by 136 publications

(69 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other techniques are socalled practical reasoning techniques, where the knowledge is encoded in forms of procedures (recipes) (e.g. (Georgeff and Lansky 1986;Myers 1996)). Relying on a sense-plan-act cycle bares the risk of not being reactive enough (especially in the early times when computation power was comparatively low).…”

Section: Methods and Models For Making Them Behave Intelligentlymentioning

confidence: 99%

Robot Controllers for Highly Dynamic Environments with Real-time Constraints

Ferrein

2010

Künstl Intell

View full text Add to dashboard Cite

Section: Methods and Models For Making Them Behave Intelligentlymentioning

confidence: 99%

Robot Controllers for Highly Dynamic Environments with Real-time Constraints

Ferrein

2010

Künstl Intell

View full text Add to dashboard Cite

“…A changing environment would make rigid and complete long term plans fairly useless. Georgeff and Lansky (1986) used an approach built on a BDI architecture that was called the procedural reasoning system (PRS), it was developed for the reaction control system of a NASA space shuttle. The system sensed the environment and had beliefs about the environment, an interpreter would look at the desires of the system and make plans, that would then be pushed to a process stack.…”

Section: Theorymentioning

confidence: 99%

On the multi-agent stochastic simulation of occupants in buildings

Chapman

Siebers

Robinson

2018

Journal of Building Performance Simulation

View full text Add to dashboard Cite

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.

show abstract

“…Once beliefs are updated, an agent selects which plan to execute by matching its beliefs with the pre-conditions in the plans. The basic execution cycle is similar to standard reactive planning systems such as PRS [12].…”

Section: Team-oriented Programmingmentioning

confidence: 99%

Coordinating Teams in Uncertain Environments: A Hybrid BDI-POMDP Approach

Nair

Tambe

2005

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Distributed partially observable Markov decision problems (POMDPs) have emerged as a popular decision-theoretic approach for planning for multiagent teams, where it is imperative for the agents to be able to reason about the rewards (and costs) for their actions in the presence of uncertainty. However, finding the optimal distributed POMDP policy is computationally intractable (NEXPComplete). This paper is focussed on a principled way to combine the two dominant paradigms for building multiagent team plans, namely the "belief-desireintention" (BDI) approach and distributed POMDPs. In this hybrid BDI-POMDP approach, BDI team plans are exploited to improve distributed POMDP tractability and distributed POMDP-based analysis improves BDI team plan performance. Concretely, we focus on role allocation, a fundamental problem in BDI teams -which agents to allocate to the different roles in the team. The hybrid BDI-POMDP approach provides three key contributions. First, unlike prior work in multiagent role allocation, we describe a role allocation technique that takes into account future uncertainties in the domain. The second contribution is a novel decomposition technique, which exploits the structure in the BDI team plans to significantly prune the search space of combinatorially many role allocations. Our third key contribution is a significantly faster policy evaluation algorithm suited for our BDI-POMDP hybrid approach. Finally, we also present experimental results from two domains: mission rehearsal simulation and RoboCupRescue disaster rescue simulation. In the RoboCupRescue domain, we show that the role allocation technique presented in this paper is capable of performing at human expert levels by comparing with the allocations chosen by humans in the actual RoboCupRescue simulation environment.

show abstract

Procedural knowledge

Cited by 136 publications

References 0 publications

Robot Controllers for Highly Dynamic Environments with Real-time Constraints

Robot Controllers for Highly Dynamic Environments with Real-time Constraints

On the multi-agent stochastic simulation of occupants in buildings

Coordinating Teams in Uncertain Environments: A Hybrid BDI-POMDP Approach

Contact Info

Product

Resources

About