A Framework for Goal-Based Semantic Compensation in Agent Systems

Unruh, Amy; Bailey, James E.; Ramamohanarao, Kotagiri

doi:10.1007/978-3-642-04879-1_10

Cited by 8 publications

(8 citation statements)

References 10 publications

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“…In the future, it would be interesting to combine the accountability model with a representation of compensations, e.g. [42], that should be executed when an outcome is not achieved. Accountability in presence of interfering actions represents another promising and equally important area to investigate.…”

Section: Discussionmentioning

confidence: 99%

MOCA: An ORM model for computational accountability

Baldoni

Baroglio

May

et al. 2019

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Accountability is often seen as a key notion in distributed systems, both in the human world and in software, on top of which interaction is built, together with the sibling notion of responsibility. However, there is limited support for the specification and use of such concepts in computational settings. This paper proposes an information model for computational accountability, which describes what data have to be available to allow, in any situation of interest arising from a group of interacting parties, the investigation and identification of accountability. The characterization of accountability provided by the model is grounded in the notions of just expectation and control. The information model is expressed in Object-Role Modeling (ORM), since it is well-suited to capture the relational nature of the accountability concept. An advantage of our model is that a designer can verify the consistency of a domain w.r.t. a set of accountability requirements. The paper exemplifies this checking by means of an Answer Set Program (ASP).

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Section: Discussionmentioning

confidence: 99%

MOCA: An ORM model for computational accountability

Baldoni

Baroglio

May

et al. 2019

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“…In this case, lacking a more sophisticated handling mechanism, the agent simply stops executing the failed abort-method with no further deliberation. The assumption we make is thus not a reflection of the full complexity of reality, but one that is pragmatic in terms of the agent execution cycle; the approach to failure-handling of [21] makes the same assumption. In systems such as SPARK, the programmer can specify an alternative behaviour for a failed failure-or abort-method by means of meta-level procedures.…”

Section: Abort Methods Invocationmentioning

confidence: 99%

“…Accordingly, dealing with failure is fundamental to agent programming, and is an important element of agent characteristics such as robustness, flexibility, and persistence [21].…”

Section: Introductionmentioning

confidence: 99%

Aborting tasks in BDI agents

Thangarajah

Harland

Morley

et al. 2007

Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems

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Intelligent agents that are intended to work in dynamic environments must be able to gracefully handle unsuccessful tasks and plans. In addition, such agents should be able to make rational decisions about an appropriate course of action, which may include aborting a task or plan, either as a result of the agent's own deliberations, or potentially at the request of another agent. In this paper we investigate the incorporation of aborts into a BDI-style architecture. We discuss some conditions under which aborting a task or plan is appropriate, and how to determine the consequences of such a decision. We augment each plan with an optional abort-method, analogous to the failure method found in some agent programming languages. We provide an operational semantics for the execution cycle in the presence of aborts in the abstract agent language CAN, which enables us to specify a BDI-based execution model without limiting our attention to a particular agent system (such as JACK, Jadex, Jason, or SPARK). A key technical challenge we address is the presence of parallel execution threads and of sub-tasks, which require the agent to ensure that the abort methods for each plan are carried out in an appropriate sequence.

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“…Thus in an agent system, a cleanup must usually be approached via semantic compensation, as rollback is not feasible [21]. A semantic-compensation-based approach can be viewed as a (default) search heuristic for replanning: often, an effective way to fix a problem is to 'reset' and then re-address the problematic goal.…”

Section: Semantic Compensationmentioning

confidence: 99%

Building more robust multi-agent systems using a log-based approach

Unruh

Bailey²,

Ramamohanarao

2009

Web Intelligence and Agent Systems: An International Journal

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In an agent system, the ability to handle problems and recover from them is important in sustaining stability and providing robustness. We claim that execution logging is essential to support agent system robustness, and that agents should have architectural-level support for logging and recovery methods. We describe an infrastructure-level, default methodology for agent problem-handling, based on logging, and supported by declaratively encoding domain-specific knowledge related to changes in goal status and semantic compensations. Via logging, the approach allows repair of already-completed as well as current goals. We define a language, APLR, to support and constrain incremental specification of problem-handling information, with the agents' problem-handling behaviour increasing in sophistication as more knowledge is added to the system. The approach is implemented by mapping the methodology and domain knowledge to 3APL-like plan rules extended to support logging.

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A Framework for Goal-Based Semantic Compensation in Agent Systems

Cited by 8 publications

References 10 publications

MOCA: An ORM model for computational accountability

MOCA: An ORM model for computational accountability

Aborting tasks in BDI agents

Building more robust multi-agent systems using a log-based approach

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