RT-BDI: A Real-Time BDI Model

Alzetta, Francesco; Giorgini, Paolo; Marinoni, Mauro; Calvaresi, Davide

doi:10.1007/978-3-030-49778-1_2

Cited by 12 publications

(15 citation statements)

References 25 publications

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“…Still in the context of BDI, Alzetta et al [5] presented a revision of the BDI model exploiting RT mechanisms named RT-BDI. The main contribution of this paper is the integration of RT mechanisms into the BDI reasoning cycle.…”

Section: Previous Attempts To Bring Real-time In Masmentioning

confidence: 99%

“…The parameters P4 and P5 (expressed in percentages) indicate the number of services and needs requiring generation with respect to the number of tasks composing the task-set. P5 is also characterized by the release time of such needs 5 . Similarly, P7, P8, and P9 are generated according to customizable ranges and related statistical distributions (e.g., uniform and Gaussian).…”

Section: Tests Setupsmentioning

confidence: 99%

See 1 more Smart Citation

Real-time multi-agent systems: rationality, formal model, and empirical results

Calvaresi

Cid

Marinoni

et al. 2021

Auton Agent Multi-Agent Syst

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Since its dawn as a discipline, Artificial Intelligence (AI) has focused on mimicking the human mental processes. As AI applications matured, the interest for employing them into real-world complex systems (i.e., coupling AI with Cyber-Physical Systems—CPS) kept increasing. In the last decades, the multi-agent systems (MAS) paradigm has been among the most relevant approaches fostering the development of intelligent systems. In numerous scenarios, MAS boosted distributed autonomous reasoning and behaviors. However, many real-world applications (e.g., CPS) demand the respect of strict timing constraints. Unfortunately, current AI/MAS theories and applications only reason “about time” and are incapable of acting “in time” guaranteeing any timing predictability. This paper analyzes the MAS compliance with strict timing constraints (real-time compliance)—crucial for safety-critical applications such as healthcare, industry 4.0, and automotive. Moreover, it elicits the main reasons for the lack of real-time satisfiability in MAS (originated from current theories, standards, and implementations). In particular, traditional internal agent schedulers (general-purpose-like), communication middlewares, and negotiation protocols have been identified as co-factors inhibiting real-time compliance. To pave the road towards reliable and predictable MAS, this paper postulates a formal definition and mathematical model of real-time multi-agent systems (RT-MAS). Furthermore, this paper presents the results obtained by testing the dynamics characterizing the RT-MAS model within the simulator MAXIM-GPRT. Thus, it has been possible to analyze the deadline miss ratio between the algorithms employed in the most popular frameworks and the proposed ones. Finally, discussing the obtained results, the ongoing and future steps are outlined.

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Section: Previous Attempts To Bring Real-time In Masmentioning

confidence: 99%

Section: Tests Setupsmentioning

confidence: 99%

Real-time multi-agent systems: rationality, formal model, and empirical results

Calvaresi

Cid

Marinoni

et al. 2021

Auton Agent Multi-Agent Syst

Self Cite

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show abstract

“…Alzetta and Giorgini contributed work toward a real-time BDI system connected to ROS 2 [1] [9]. Their implementation uses a custom built BDI engine, implemented in C++, which supports soft realtime constraints.…”

Section: Related Workmentioning

confidence: 99%

Toward Campus Mail Delivery Using BDI

Onyedinma

Gavigan

Esfandiari

2020

Electron. Proc. Theor. Comput. Sci.

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Autonomous systems developed with the Belief-Desire-Intention (BDI) architecture are usually mostly implemented in simulated environments. In this project we sought to build a BDI agent for use in the real world for campus mail delivery in the tunnel system at Carleton University. Ideally, the robot should receive a delivery order via a mobile application, pick up the mail at a station, navigate the tunnels to the destination station, and notify the recipient. We linked the Robot Operating System (ROS) with a BDI reasoning system to achieve a subset of the required use cases. ROS handles the low-level sensing and actuation, while the BDI reasoning system handles the high-level reasoning and decision making. Sensory data is orchestrated and sent from ROS to the reasoning system as perceptions. These perceptions are then deliberated upon, and an action string is sent back to ROS for interpretation and driving of the necessary actuator for the action to be performed. In this paper we present our current implementation, which closes the loop on the hardwaresoftware integration, and implements a subset of the use cases required for the full system.

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“…The upper layer, called "Social Layer", contains the distributed application whereas the bottom layers contain classic robot functionality and the software adapters to the social layer. In contrast, the authors of [20] use ROS2 as a communication layer for multi-agent systems. They focus on the application layer by developing the Belief-Desire-Intention (BDI) behavior model on top of ROS2.…”

Section: Related Workmentioning

confidence: 99%

Robotic Systems of Systems Based on a Decentralized Service-Oriented Architecture

et al. 2020

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Multi-robot systems are often static and pre-configured during the design time of their software. Emerging cooperation between unknown robots is still rare and limited. Such cooperation might be basic like sharing sensor data or complex like conjoined motion planning and acting. Robots should be able to detect other robots and their abilities during runtime. When cooperation seems to be possible and beneficial, it should be initiated autonomously. A centralized cloud control shall be avoided. Using software patterns belonging to service-oriented architectures, the robots are able to discover other robots and their abilities during runtime. These abilities are implemented as services and described by their interfaces. Composition of services can be done easily and flexibly. The software patterns originally belonging to cloud computing could be successfully adopted to decentralized multi-robot systems. The developed concept allows autonomous systems to cooperate flexibly and to compose multi-robot systems during runtime.

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RT-BDI: A Real-Time BDI Model

Cited by 12 publications

References 25 publications

Real-time multi-agent systems: rationality, formal model, and empirical results

Real-time multi-agent systems: rationality, formal model, and empirical results

Toward Campus Mail Delivery Using BDI

Robotic Systems of Systems Based on a Decentralized Service-Oriented Architecture

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