Temporal Models for History-Aware Explainability

Parra-Ullauri, Juan Marcelo; García-Domínguez, Antonio; Garcia-Paucar, Luis Hernan; Bencomo, Nelly

doi:10.1145/3419804.3420276

Cited by 15 publications

(20 citation statements)

References 37 publications

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“…Specifically, MR-POMDP++ can be used during simulations to further learn about the environment of the AIS, and to uncover unforeseen contexts that may be difficult otherwise, to envisage in advance. Further, as the approach helps to discover further knowledge about a system's behaviour and its environment based on runtime evidence, it can also be used to provide post-hoc explanations for AISs' behaviour [16].…”

Section: Discussionmentioning

confidence: 99%

Towards priority-awareness in autonomous intelligent systems

Samin

Paucar

Bencomo

et al. 2021

Proceedings of the 36th Annual ACM Symposium on Applied Computing

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In Autonomous and Intelligent systems (AIS), the decision-making process can be divided into two parts: (i) the priorities of the requirements are determined at design-time; (ii) design selection follows where alternatives are compared, and the preferred alternatives are chosen autonomously by the AIS. Runtime design selection is a trade-off analysis between non-functional requirements (NFRs) that uses optimisation methods, including decision-analysis and utility theory. The aim is to select the design option yielding the highest expected utility. A problem with these techniques is that they use a uni-scalar cumulative utility value to represent a combined priority for all the NFRs. However, this uni-scalar value doesn't give information about the varying impacts of actions under uncertain environmental contexts on the satisfaction priorities of individual NFRs. In this paper, we present a novel use of Multi-Reward Partially Observable Markov Decision Process (MR-POMDP) to support reasoning of separate NFR priorities. We discuss the use of rewards in MR-POMDPs as a way to support AIS with (a) priorityaware decision-making; and (b) maintain service-level agreement, by autonomously tuning NFRs' priorities to new contexts and based on data gathered at runtime. We evaluate our approach by applying it to a substantial Network case. CCS CONCEPTS• Computing methodologies → Probabilistic reasoning; • Theory of computation → Sequential decision making; • Software and its engineering → Extra-functional properties; Designing software;

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Section: Discussionmentioning

confidence: 99%

Towards priority-awareness in autonomous intelligent systems

Samin

Paucar

Bencomo

et al. 2021

Proceedings of the 36th Annual ACM Symposium on Applied Computing

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“…Further, flexibility can also be introduced. For example, techniques based on TGs [29] can store the long-term history of a running system to support querying, at the expense of disk space and processing time. Another technique that supports monitoring is CEP [22], which can quickly react to incoming situations efficiently, if the appropriate event patterns have been previously identified.…”

Section: Baseline 21 Runtime Service Monitoringmentioning

confidence: 99%

Towards an architecture integrating complex event processing and temporal graphs for service monitoring

Parra-Ullauri

García-Domínguez

Boubeta-Puig

et al. 2021

Proceedings of the 36th Annual ACM Symposium on Applied Computing

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Software is becoming more complex as it needs to deal with an increasing number of aspects in volatile environments. This complexity may cause behaviors that violate the imposed constraints. A goal of runtime service monitoring is to determine whether the service behaves as intended to potentially allow the correction of the behavior. It may be set up in advance the infrastructure to allow the detections of suspicious situations. However, there may also be unexpected situations to look for as they only become evident during data stream monitoring at runtime produced by te system. The access to historic data may be key to detect relevant situations in the monitoring infrastructure. Available technologies used for monitoring offer different trade-offs, e.g. in cost and flexibility to store historic information. For instance, Temporal Graphs (TGs) can store the long-term history of an evolving system for future querying, at the expense of disk space and processing time. In contrast, Complex Event Processing (CEP) can quickly react to incoming situations efficiently, as long as the appropriate event patterns have been set up in advance. This paper presents an architecture that integrates CEP and TGs for service monitoring through the data stream produced at runtime by a system. The pros and cons of the proposed architecture for extracting and treating the monitored data are analyzed. The approach is applied on the monitoring of Quality of Service (QoS) of a data-management network case study. It is demonstrated how the architecture provides rapid detection of issues, as well as the ability to access to historical data about the state of the system to allow for a comprehensive monitoring solution. CCS CONCEPTS• Software and its engineering → Maintaining software.

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“…A relevant self-awareness aspect is (self-) history-awareness. In [30], the authors argue that systems should be able to access their adaptation history to tailor future adaptations accordingly, and they call it history-awareness (HA). Further, they present four levels of HA that serve to frame explanation capabilities: 1) forensic self-explanation, 2) live HA explanation, 3) externally-guided and HA decision-making with explanation capabilities, and 4) automated HA systems.…”

Section: Autonomous and Self-aware Systemsmentioning

confidence: 99%

“…Parra et al [30] showed a system that could turn the structured logs of a system into a temporal graph, giving users the ability to write structured queries about its evolution. They framed their approach into a gradual 4-level roadmap for introducing timeawareness into self-adaptive systems, and placed themselves at level 2 (live history-aware explanations).…”

Section: Related Workmentioning

confidence: 99%

Towards automated provenance collection for runtime models to record system history

Reynolds

García-Domínguez

Bencomo

2020

Proceedings of the 12th System Analysis and Modelling Conference

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In highly dynamic environments, systems are expected to make decisions on the fly based on their observations that are bound to be partial. As such, the reasons for its runtime behaviour may be difficult to understand. In these cases, accountability is crucial, and decisions by the system need to be traceable. Logging is essential to support explanations of behaviour, but it poses challenges. Concerns about analysing massive logs have motivated the introduction of structured logging, however, knowing what to log and which details to include is still a challenge. Structured logs still do not necessarily relate events to each other, or indicate time intervals. We argue that logging changes to a runtime model in a provenance graph can mitigate some of these problems. The runtime model keeps only relevant details, therefore reducing the volume of the logs, while the provenance graph records causal connections between the changes and the activities performed by the agents in the system that have introduced them. In this paper, we demonstrate a first version towards a reusable infrastructure for the automated construction of such a provenance graph. We apply it to a multithreaded traffic simulation case study, with multiple concurrent agents managing different parts of the simulation. We show how the provenance graphs can support validating the system behaviour, and how a seeded fault is reflected in the provenance graphs. CCS CONCEPTS • Software and its engineering → System modeling languages; Integration frameworks; Model-driven software engineering.

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Temporal Models for History-Aware Explainability

Cited by 15 publications

References 37 publications

Towards priority-awareness in autonomous intelligent systems

Towards priority-awareness in autonomous intelligent systems

Towards an architecture integrating complex event processing and temporal graphs for service monitoring

Towards automated provenance collection for runtime models to record system history

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