Allocating structured tasks in heterogeneous agent teams

Baségio, Túlio Lima; Bordini, Rafael H.

doi:10.1111/coin.12194

Cited by 6 publications

(3 citation statements)

References 48 publications

(103 reference statements)

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“…becomes achievable again). 8 Updates to existing intentions are primarily of two kinds: the extension of an intention that posts a sub-goal with a plan to achieve the goal; and the modification of an intention when the plan selected to achieve the current sub-goal fails, which typically involves replacing the current plan for the sub-goal (or a parent goal of the sub-goal) with a new plan. As with belief and goal updates, updating the set of intentions may involve sophisticated reasoning or deliberation.…”

Section: The Bdi Cyclementioning

confidence: 99%

“…This can be used for example to achieve organisational goals, find a specific course of action that will allow the agent to achieve a number of goals given its plan library, or to provide alternatives in case JaCaMo social plans fail through a combination of task allocation, individual planning, and runtime coordination through JaCaMo artifacts. To support goal allocation before HTN planning is carried out, Cardoso and Bordini [29] make use of plan-library metrics as heuristics, while Baségio and Bordini [8] propose a task allocation mechanism for JaCaMo systems.…”

Section: Ai In the Plan Phasementioning

confidence: 99%

See 1 more Smart Citation

Agent programming in the cognitive era

Bordini

Seghrouchni

Hindriks

et al. 2020

Auton Agent Multi-Agent Syst

Self Cite

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It is claimed that, in the nascent 'Cognitive Era', intelligent systems will be trained using machine learning techniques rather than programmed by software developers. A contrary point of view argues that machine learning has limitations, and, taken in isolation, cannot form the basis of autonomous systems capable of intelligent behaviour in complex environments. In this paper, we explore the contributions that agent-oriented programming can make to the development of future intelligent systems. We briefly review the state of the art in agent programming, focussing particularly on BDI-based agent programming languages, and discuss previous work on integrating AI techniques (including machine learning) in agent-oriented programming. We argue that the unique strengths of BDI agent languages provide an ideal framework for integrating the wide range of AI capabilities necessary for progress towards the next-generation of intelligent systems. We identify a range of possible approaches to integrating AI into a BDI agent architecture. Some of these approaches, e.g., 'AI as a service', exploit immediate synergies between rapidly maturing AI techniques and agent programming, while others, e.g., 'AI embedded into agents' raise more fundamental research questions, and we sketch a programme of research directed towards identifying the most appropriate ways of integrating AI capabilities into agent programs.

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Section: The Bdi Cyclementioning

confidence: 99%

Section: Ai In the Plan Phasementioning

confidence: 99%

Agent programming in the cognitive era

Bordini

Seghrouchni

Hindriks

et al. 2020

Auton Agent Multi-Agent Syst

Self Cite

View full text Add to dashboard Cite

show abstract

“…Note that, when tasks have different task's durations, they prove that their algorithm has an approximation ratio of 2 for this NP-hard problem. Basegio and Bordini propose a decentralized task allocation algorithm, and consider heterogeneous task allocation for heterogeneous agent teams [27]. Moreover, they test their algorithm in various conditions, and discuss the future challenges.…”

Section: Related Work a Multi-robot Task Allocationmentioning

confidence: 99%

Multi-Robot Cooperative Task Allocation With Definite Path-Conflict-Free Handling

et al. 2019

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Modeling and solving multi-robot task allocation with definite path-conflict-free handling is an important research, especially in real working environments. Some of the research lines are unable to obtain definite path-conflict-free solutions for multi-robot task allocations, such as using the penalty-term method in the fitness function to restrict the survival probabilities of the solutions with path conflicts. In some cases, these solutions are only able to satisfy the objective of minimizing task time. We formulate this problem based on grid maps, while focusing on the frequently used cooperative task allocation. In our model, two subtasks of each cooperative task must be executed by two robots, simultaneously. We propose vitality-driven genetic task allocation algorithm (VGTA), which is able to simultaneously minimize task time and realize definite conflict-free path planning. VGTA consists of local operators, such as random mutations, greedy crossovers, and vitality selection. Meanwhile, VGTA includes schedule conflict and path conflict handling strategies. In path conflict handling strategy, we not only consider the common path conflicts in a grid cell, but also focus on the path conflicts between robots when exchanging positions in the adjacent grid cells. Besides, we construct our benchmarks based on real working environments, such as factory, powerhouse, and airport environments. Experimental results indicate that VGTA's search capability and computation cost are satisfactory. Meanwhile, its solutions are able to be really executed.INDEX TERMS Multi-robot task allocation, cooperative task, schedule conflict, path conflict, unmanned multi-robot swarm.

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Robustness of decentralized decision‐making architectures in command and control systems

Boss

Gralla

2022

Systems Engineering

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Organizations use command and control (C2) systems to collect, organize, and disseminate information in order to make decisions, impart instructions, and manage resources to accomplish a mission. C2 agility and robustness are critical to ensure that a C2 system can perform well in a variety of environments. One system design principle, decentralization, has been closely linked to desirable system characteristics such as agility and adaptability, but its relationship to system performance robustness is not well‐established. In this study, we explore C2 system architectures—ranging from fully centralized to fully decentralized archetypes—to assess their performance and robustness characteristics across a spectrum of operating environments. While the centralized archetype achieves high performance in favorable environmental conditions, its performance quickly degrades when conditions worsen, hindering its overall robustness. Conversely, the decentralized archetype achieves a lower but more stable performance profile resulting in more robustness when performance requirements are lower. Finally, we explore alternative, hybrid architectures with varying degrees of centralized and decentralized decision‐making. We find that by centralizing only certain system‐consequential functions such as resource allocation, and decentralizing more focused decision functions which can be performed suitably with only local information and resources, system performance and robustness are improved beyond that of the simple archetypes.

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Allocating structured tasks in heterogeneous agent teams

Cited by 6 publications

References 48 publications

Agent programming in the cognitive era

Agent programming in the cognitive era

Multi-Robot Cooperative Task Allocation With Definite Path-Conflict-Free Handling

Robustness of decentralized decision‐making architectures in command and control systems

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