Cooperative Monitoring to Diagnose Multiagent Plans

Abstract

Diagnosing the execution of a Multiagent Plan (MAP) means identifying and explaining action failures (i.e., actions that did not reach their expected effects). Current approaches to MAP diagnosis are substantially centralized, and assume that action failures are independent of each other.In this paper, the diagnosis of MAPs, executed in a dynamic and partially observable environment, is addressed in a fully distributed and asynchronous way; in addition, action failures are no longer assumed as independent of each other.The paper presents a novel methodology, named Cooperative Weak-Committed Monitoring (CWCM), enabling agents to cooperate while monitoring their own actions. Cooperation helps the agents to cope with very scarcely observable environments: what an agent cannot observe directly can be acquired from other agents. CWCM exploits nondeterministic action models to carry out two main tasks: detecting action failures and building trajectory-sets (i.e., structures representing the knowledge an agent has about the environment in the recent past). Relying on trajectory-sets, each agent is able to explain its own action failures in terms of exogenous events that have occurred during the execution of the actions themselves. To cope with dependent failures, CWCM is coupled with a diagnostic engine that distinguishes between primary and secondary action failures.An experimental analysis demonstrates that the CWCM methodology, together with the proposed diagnostic inferences, are effective in identifying and explaining action failures even in scenarios where the system observability is significantly reduced.

Accountability for Practical Reasoning Agents

“…Researchers in multi-agent systems and deontic logic have addressed the concept of responsibility as the problem of assigning blame for failures of group plans or norms [25][26][27][28][29][30][31][32][33][34][35][36]. This problem has been well studied in the literature, and as determining responsibility is a process performed by a principal, it is largely orthogonal to our focus in this paper: the capabilities needed for an accountable agent to play its role in an accountability relationship with a principal.…”

Multi-Robot Assembly Strategies and Metrics

“…Specifically, physical conflicts such as a lack of temporal or spatial synchronization, collisions, or the excessive build-up of internal stresses or forces of shared workpieces are likely the result of errors or uncertainty in the robots’ software coordination. At the team level, performance metrics have been proposed to evaluate system-wide errors in multi-robot planning (e.g., Sellner et al (2006), Korsah et al (2013), and Micalizio and Torasso (2014)), communications (Kalech 2012), and conflict negotiations (Kalech and Kaminka 2007). Alternatively, at the individual robot level, performance errors may result from shortcomings in software agility, possibly inferred based on the robot’s measured efficiency and effectiveness (Downs et al 2016).…”

Explaining interdependent action delays in multiagent plans execution

“…The framework proposed by Micalizio et al [20,21,24] can be considered atemporal, as well, but it differs from the previous one in the action models used during the diagnostic inferences, which are represented as relations defined over a set of state variables. Relational action models, thus, do not represent only the nominal behavior, but one or more faulty behaviors.…”

“…An explanation can be given just in terms of actions labeled as failed [38], or in terms of faults representing the root causes for the action failures [21]. In [24] a diagnosis is given in terms of a set of trajectories, defined over the state variables of a given agent, each of which describes a possible (i.e., consistent) explanation for the agent's observed behavior.…”