Ben Rachmut - Academia.edu (original) (raw)

Papers by Ben Rachmut

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Multi-agent task allocation in physical environments with spatial and temporal constraints, are h... more Multi-agent task allocation in physical environments with spatial and temporal constraints, are hard problems that are relevant in many realistic applications. A task allocation algorithm based on Fisher market clearing (FMC_TA), that can be performed either centrally or distributively, has been shown to produce high quality allocations in comparison to both centralized and distributed state of the art incomplete optimization algorithms. However, the algorithm is synchronous and therefore depends on perfect communication between agents. We propose FMC_ATA, an asynchronous version of FMC_TA, which is robust to message latency and message loss. In contrast to the former version of the algorithm, FMC_ATA allows agents to identify dynamic events and initiate the generation of an updated allocation. Thus, it is more compatible for dynamic environments. We further investigate the conditions in which the distributed version of the algorithm is preferred over the centralized version. Our re...

Max-sum is a version of belief propagation that was adapted for solving distributed constraint op... more Max-sum is a version of belief propagation that was adapted for solving distributed constraint optimization problems (DCOPs). It has been studied theoretically and empirically, extended to versions that improve solution quality and converge rapidly, and is applicable to multiple distributed applications. The algorithm was presented both as a synchronous and an asynchronous algorithm, however, neither the differences in the performance of these two execution versions nor the implications of imperfect communication (i.e., massage delay and message loss) on the two versions, have been investigated to the best of our knowledge. We contribute to the body of knowledge on Max-sum by: (1) Establishing the theoretical differences between the two execution versions of the algorithm, focusing on the construction of beliefs; (2) Empirically evaluating the differences between the solutions generated by the two versions of the algorithm, with and without message delay or loss; and (3) Establishin...

Journal of Artificial Intelligence Research

Most studies investigating models and algorithms for distributed constraint optimization problems... more Most studies investigating models and algorithms for distributed constraint optimization problems (DCOPs) assume that messages arrive instantaneously and are never lost. Specifically, distributed local search DCOP algorithms, have been designed as synchronous algorithms (i.e., they perform in synchronous iterations in which each agent exchanges messages with all its neighbors), despite running in asynchronous environments. This is true also for an anytime mechanism that reports the best solution explored during the run of synchronous distributed local search algorithms. Thus, when the assumption of perfect communication is relaxed, the properties that were established for the state-of-the-art local search algorithms and the anytime mechanism may not necessarily apply. In this work, we address this limitation by: (1) Proposing a Communication-Aware DCOP model (CA-DCOP) that can represent scenarios with different communication disturbances; (2) Investigating the performance of existin...

Most studies investigating models and algorithms for distributed constraint optimization problems... more Most studies investigating models and algorithms for distributed constraint optimization problems (DCOPs) assume messages arrive instantaneously or within a (short) bounded delay. Specifically, distributed local searchDCOP algorithms have been designed as synchronous algorithms, performing in an asynchronous environment, i.e., algorithms that perform in synchronous iterations in which each agent exchanges messages with all its neighbors. This is true also for an anytimemechanism that reports the best solution explored during the run of synchronous distributed local search algorithms. Thus, when the assumptions on instantaneous message arrival are relaxed, the state of the art local search algorithms and mechanism do not apply. In this work, we address this limitation by: (1) Investigating the performance of existing local search DCOP algorithms in the presence of message latency; (2) Proposing an asynchronous monotonic distributed local search DCOP algorithm; and (3) Proposing an as...

11 Max-sum is a version of belief propagation that was adapted for solving distributed constraint... more 11 Max-sum is a version of belief propagation that was adapted for solving distributed constraint optimization 12 problems (DCOPs). It has been studied theoretically and empirically, extended to versions that improve solution 13 quality and converge rapidly, and is applicable to multiple distributed applications. The algorithm was presented 14 both as a synchronous and an asynchronous algorithm, however, neither the differences in the performance of 15 these two execution versions nor the implications of message latency on the two versions have been investigated 16 to the best of our knowledge. 17 We contribute to the body of knowledge on Max-sum by: (1) Establishing the theoretical differences between 18 the two execution versions of the algorithm, focusing on the construction of beliefs; (2) Empirically evaluating 19 the differences between the solutions generated by the two versions of the algorithm, with and without message 20 latency; and (3) Establishing both theoretically and...

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Multi-agent task allocation in physical environments with spatial and temporal constraints, are h... more Multi-agent task allocation in physical environments with spatial and temporal constraints, are hard problems that are relevant in many realistic applications. A task allocation algorithm based on Fisher market clearing (FMC_TA), that can be performed either centrally or distributively, has been shown to produce high quality allocations in comparison to both centralized and distributed state of the art incomplete optimization algorithms. However, the algorithm is synchronous and therefore depends on perfect communication between agents. We propose FMC_ATA, an asynchronous version of FMC_TA, which is robust to message latency and message loss. In contrast to the former version of the algorithm, FMC_ATA allows agents to identify dynamic events and initiate the generation of an updated allocation. Thus, it is more compatible for dynamic environments. We further investigate the conditions in which the distributed version of the algorithm is preferred over the centralized version. Our re...

Max-sum is a version of belief propagation that was adapted for solving distributed constraint op... more Max-sum is a version of belief propagation that was adapted for solving distributed constraint optimization problems (DCOPs). It has been studied theoretically and empirically, extended to versions that improve solution quality and converge rapidly, and is applicable to multiple distributed applications. The algorithm was presented both as a synchronous and an asynchronous algorithm, however, neither the differences in the performance of these two execution versions nor the implications of imperfect communication (i.e., massage delay and message loss) on the two versions, have been investigated to the best of our knowledge. We contribute to the body of knowledge on Max-sum by: (1) Establishing the theoretical differences between the two execution versions of the algorithm, focusing on the construction of beliefs; (2) Empirically evaluating the differences between the solutions generated by the two versions of the algorithm, with and without message delay or loss; and (3) Establishin...

Journal of Artificial Intelligence Research

Most studies investigating models and algorithms for distributed constraint optimization problems... more Most studies investigating models and algorithms for distributed constraint optimization problems (DCOPs) assume that messages arrive instantaneously and are never lost. Specifically, distributed local search DCOP algorithms, have been designed as synchronous algorithms (i.e., they perform in synchronous iterations in which each agent exchanges messages with all its neighbors), despite running in asynchronous environments. This is true also for an anytime mechanism that reports the best solution explored during the run of synchronous distributed local search algorithms. Thus, when the assumption of perfect communication is relaxed, the properties that were established for the state-of-the-art local search algorithms and the anytime mechanism may not necessarily apply. In this work, we address this limitation by: (1) Proposing a Communication-Aware DCOP model (CA-DCOP) that can represent scenarios with different communication disturbances; (2) Investigating the performance of existin...

Most studies investigating models and algorithms for distributed constraint optimization problems... more Most studies investigating models and algorithms for distributed constraint optimization problems (DCOPs) assume messages arrive instantaneously or within a (short) bounded delay. Specifically, distributed local searchDCOP algorithms have been designed as synchronous algorithms, performing in an asynchronous environment, i.e., algorithms that perform in synchronous iterations in which each agent exchanges messages with all its neighbors. This is true also for an anytimemechanism that reports the best solution explored during the run of synchronous distributed local search algorithms. Thus, when the assumptions on instantaneous message arrival are relaxed, the state of the art local search algorithms and mechanism do not apply. In this work, we address this limitation by: (1) Investigating the performance of existing local search DCOP algorithms in the presence of message latency; (2) Proposing an asynchronous monotonic distributed local search DCOP algorithm; and (3) Proposing an as...

11 Max-sum is a version of belief propagation that was adapted for solving distributed constraint... more 11 Max-sum is a version of belief propagation that was adapted for solving distributed constraint optimization 12 problems (DCOPs). It has been studied theoretically and empirically, extended to versions that improve solution 13 quality and converge rapidly, and is applicable to multiple distributed applications. The algorithm was presented 14 both as a synchronous and an asynchronous algorithm, however, neither the differences in the performance of 15 these two execution versions nor the implications of message latency on the two versions have been investigated 16 to the best of our knowledge. 17 We contribute to the body of knowledge on Max-sum by: (1) Establishing the theoretical differences between 18 the two execution versions of the algorithm, focusing on the construction of beliefs; (2) Empirically evaluating 19 the differences between the solutions generated by the two versions of the algorithm, with and without message 20 latency; and (3) Establishing both theoretically and...