A plan carried on in the real world may be affected by a number of unexpected events, plan threats, which cause significant deviations between the intended behavior of the plan executor (i.e., the agent) and the observed one. These deviations are typically considered as action failures.This paper addresses the problem of recovering from action failures caused by a specific class of plan threats: faults in the functionalities of the agent. The problem is approached by exploiting techniques of the Model-Based Diagnosis (MBD) for detecting failures (plan execution monitoring) and for explaining these failures in terms of faulty functionalities (agent diagnosis). The recovery process is modeled as a replanning problem aimed at fixing the faulty components identified by the agent diagnosis. However, since the diagnosis is in general ambiguous (a failure may be explained by alternative faults), the recovery has to deal with such an uncertainty. The paper advocates the adoption of a conformant planner, which guarantees that the recovery plan, if it exists, is executable no matter what the actual cause of the failure is.The paper focuses on a single agent performing its own plan, however the proposed methodology takes also into account that agents are typically situated into a multi-agent scenario and that commitments between agents may exist. The repair strategy is therefore conceived to overcome the causes of a failure while assuring the commitments an agent has agreed with other team members.

We present the JaCaMo+ framework for programming multiagent systems (MAS), where agents interact thanks to commitment-based interaction protocols. Commitment protocols are realized as artifacts that maintain a social state and notify to the participating agents those events that are relevant to the interaction. We discuss the advantages, like increased modularity and flexibility, that are brought by commitment-ruled interactions with respect to other proposals. We trace back such advantages to the possibility of relying on a standardized commitment lifecycle. We explain how to use the framework to program interacting agents by using the Netbill protocol as running example, and the Gold Miners scenario as a more complex programming example.

In this paper we address the problem of diagnosing the execution of Multiagent Plans with interdependent action delays. To this end, we map our problem to the Model-Based Diagnosis setting, and solve it by devising a novel modeling and reasoning method to infer preferred diagnoses based on partial observation of the start and end times of plan actions. Interestingly, we show that the kind of problem we address can be seen as an extension to the well known disjunctive temporal problem with preferences, augmented with a (qualitative) Bayesian network that models dependencies among action delays. An extensive set of tests performed with a prototype implementation on two different problem domains proves the feasibility of the proposed methodology.

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.

Abstract. The paper addresses the problem of supervising the execution of a plan with durative actions in a just partially known world, where discrepancies between the expected conditions and the ones actually found may arise. The paper advocates a control architecture which exploits additional knowledge to prevent (when possible) action failures by changing the execution modality of actions while these are still in progress. Preliminary experimental results, obtained in a simulated space exploration scenario, are reported.