Challenges for Exception Handling in Multi-Agent Systems

Platon, Éric; Honiden, Shinichi

doi:10.1007/978-3-540-73131-3_3

Cited by 6 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More recently Platon et al [25,26] have systematically and methodically examined different types of agent failures (exceptions), as well as resulting coordination failures and their causes (e.g., those due to agent death, knowledge inconsistencies, etc.). They propose a number of ways for integrating failure handling capabilities into the agent architecture.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

Detecting disagreements in large-scale multi-agent teams

Kaminka

2008

Auton Agent Multi-Agent Syst

View full text Add to dashboard Cite

Intermittent sensory, actuation and communication failures may cause agents to fail in maintaining their commitments to others. Thus to collaborate robustly, agents must monitor others to detect coordination failures. Previous work on monitoring has focused mainly on small-scale systems, with only a limited number of agents. However, as the number of monitored agents is scaled up, two issues are raised that challenge previous work. First, agents become physically and logically disconnected from their peers, and thus their ability to monitor each other is reduced. Second, the number of possible coordination failures grows exponentially, with all potential interactions. Thus previous techniques that sift through all possible failure hypotheses cannot be used in large-scale teams. This paper tackles these challenges in the context of detecting disagreements among team-members, a monitoring task that is of particular importance to robust teamwork. First, we present new bounds on the number of agents that must be monitored in a team to guarantee disagreement detection. These bounds significantly reduce the connectivity requirements of the monitoring task in the distributed case. Second, we present YOYO, a highly scalable disagreement-detection algorithm which guarantees sound detection. YOYO's run-time scales linearly in the number of monitored agents, despite the exponential number of hypotheses. It compactly represents all valid hypotheses in single structure, while allowing for a complex hierarchical organizational structure to be considered in the monitoring. Both YOYO and the new bounds are explored analytically and empirically in monitoring problems involving thousands of agents.

show abstract

Section: Motivation and Backgroundmentioning

confidence: 99%

Detecting disagreements in large-scale multi-agent teams

Kaminka

2008

Auton Agent Multi-Agent Syst

View full text Add to dashboard Cite

show abstract

“…There is MAS work focused on exception handling in multiagent contexts: Klein and Dellarocas [37] propose a shared exception handling service that other agents turn to for help recovering from a problem, such as when a plan fails or a garbled message is received. Platon et al [44] surveyed the challenges for exception handling in MAS, and Platon's thesis is on exception handling and a robust framework for executing agent plans [44]. Lam et al [39] describe a workflow management system that uses norms and semantic web techniques to handle exceptions that arise during enactment.…”

Section: Related Workmentioning

confidence: 99%

Mandrake: multiagent systems as a basis for programming fault-tolerant decentralized applications

Christie

Chopra

Singh

2022

Auton Agent Multi-Agent Syst

View full text Add to dashboard Cite

We conceptualize a decentralized software application as one constituted from autonomous agents that communicate via asynchronous messaging. Modern software paradigms such as microservices and settings such as the Internet of Things evidence a growing interest in decentralized applications. Constructing a decentralized application involves designing agents as independent local computations that coordinate successfully to realize the application’s requirements. Moreover, a decentralized application is susceptible to faults manifested as message loss, delay, and reordering. We contribute Mandrake, a programming model for decentralized applications that tackles these challenges without relying on infrastructure guarantees. Specifically, we adopt the construct of an information protocol that specifies messaging between agents purely in causal terms and can be correctly enacted by agents in a shared-nothing environment over nothing more than unreliable, unordered transport. Mandrake facilitates (1) implementing protocol-compliant agents by introducing a programming model; (2) transforming protocols into fault-tolerant ones with simple annotations; and (3) a declarative policy language that makes it easy to implement fault-tolerance in agents based on the capabilities in protocols. Mandrake’s significance lies in demonstrating a straightforward approach for constructing decentralized applications without relying on coordination mechanisms in the infrastructure, thus achieving some of the goals of the founders of networked computing from the 1970s.

show abstract