An Innovative Data-Driven and Adaptive Reinforcement Learning Approach for Context-Aware Prescriptive Process Monitoring

Homayoun Najjaran; Maryam Ahang; Maziyar Khadivi; Mostafa Abbasi; Patricia Lasserre; Yves Lucet

REVIEW 4 major objections 6 minor 1 cited by

An Innovative Data-Driven and Adaptive Reinforcement Learning Approach for Context-Aware Prescriptive Process Monitoring

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A five-step offline reinforcement learning framework claims 31% resource-time savings and 23% shorter process spans by reordering activities in business workflows.

desk verdict The combination of offline Q-learning, reward shaping, and process-aware augmentation is genuinely new, but the headline KPI savings are unestablished because the replay simulator is never specified and the paper contradicts itself on offline vs online fine-tuning. read the letter →

arxiv 2501.10543 v2 pith:4WHJ2FYV submitted 2025-01-17 cs.LG cs.AI

Mostafa Abbasi , Maziyar Khadivi , Maryam Ahang , Patricia Lasserre , Yves Lucet , Homayoun Najjaran This is my paper · ORCID

classification cs.LGcs.AI

keywords prescriptiveprocessmonitoringofflinereinforcementlearningprocess-awaredataaugmentationrewardshapingbestnextactivityQ-learningeventlogsmining

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper proposes FORLAPS, a five-step framework that uses offline reinforcement learning with a state-dependent reward function to reorder activities in business processes, and claims it cuts resource time by 31% and total process span by 23% on a real event log from a Canadian energy regulator. The key idea is to penalize a disapproved activity more heavily the later in the trace it occurs, so the learned policy front-loads risky tasks and short-circuits cascading failures. To compensate for scarce historical data, the paper adds process-aware data augmentation that preserves business rules, enabling offline fine-tuning without online interaction. If the reported savings hold, the framework provides a practical decision-support tool for prescriptive process monitoring that goes beyond prediction to recommend optimal next steps.

What carries the argument

The central object is the state-dependent reward-shaping function $R(s_t,a_t)=-r\,|s_t|\,\mathbb{I}(s_t,a_t)$, which returns a penalty proportional to the number of activities already executed when the chosen activity leads to a KPI violation. This length-aware penalty is what makes the Q-learning agent prefer to surface risky activities early. The second load-bearing component is the process-aware data augmentation: timestamps are shifted by up to 10% of inter-activity time, non-critical activities are selectively removed from completed groups, and the synthetic traces are filtered against business-rule constraints, allowing the pre-trained Q-function to be fine-tuned offline as if exploration had continued.

What would settle it

Replay the learned policy on the energy-regulator event log while keeping each activity's real per-activity duration and order-dependent approval probabilities; if reordered traces do not reduce resource time by 31% and process span by 23% (or if they increase either), the central claim collapses.

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Extended reading notes

Core claim

On its own terms, the paper establishes that a Q-learning agent over process states, trained with a length-aware reward $R(s_t,a_t)=-r\,|s_t|\,\mathbb{I}(s_t,a_t)$ and fine-tuned on augmented traces, learns to recommend activity orders that outperform both a multi-task LSTM next-action recommender and a permutation-feature-importance baseline. When 15,000 test-set traces are replayed with the learned policy, the paper reports 31% savings in resource time spent and a 23% reduction in process time span, with an average 2.66 fewer days of resource time and 9.69 fewer days per case compared with a baseline of 8.52 days per activity and 40.56 days per case. The framework also yields a ranked set of best next activities rather than a single action, and its robustness is assessed with Damerau–Levenshtein distance across nine public event logs from healthcare, finance, permits, and operations.

Load-bearing premise

The savings numbers assume that replaying 15,000 traces with the learned policy is a valid counterfactual: activities can be reordered freely, their durations and approval outcomes are order-independent, and an early disapproval allows later activities to be skipped.

Editorial extensions

If this is right

Applied to new applications at the energy regulator, the learned policy could reduce resource time spent on reviews by roughly a third and total approval time by about a quarter, if the replay assumptions hold.
Because fine-tuning is fully offline, the same recipe transfers to domains where online interaction is expensive, such as healthcare treatment pathways and permit processing.
The policy does not prescribe one rigid sequence; it outputs a ranked list of next-best activities, so decision-makers can choose among near-optimal actions under operational constraints.
The Damerau–Levenshtein validation indicates the framework both reproduces traces that lead to desired outcomes and avoids traces that lead to undesired ones, across different prefix lengths.
Performance is strongest when the event log has dense state coverage relative to its number of activities; sparse logs such as BPIC_2015 are an explicit boundary of the method.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The 31% and 23% figures depend on a replay counterfactual that the paper never fully specifies; if activity durations or approval outcomes are order-dependent in the real process, the realized savings could differ even if the Q-learning converged correctly.
The same reward-shaping principle — penalizing late failure linearly in trace length — could be ported to other continuous KPIs such as cost or defect counts, as long as the state encodes those dimensions.
A natural next test is a real A/B trial where the policy's reorderings are applied to a random subset of cases; if early disapproval does not reliably cancel downstream work, the resource-time gain shrinks.
The analogy to fast-fail in manufacturing suggests the framework may transfer well beyond business process mining, to any sequential task where late-stage failure is disproportionately expensive.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: the method is a reasonable combination of known pieces, but the headline numbers don't hold up as reported because the replay simulator behind them is never defined, and the paper contradicts itself on whether the fine-tuning is offline or online.

The genuinely new part is the specific recipe: tabular offline Q-learning with state-dependent reward shaping, plus process-aware data augmentation that respects precedence and co-occurrence rules, plus a fine-tuning phase. Applying it to nine public datasets is more breadth than most prior prescriptive monitoring work, and the Damerau-Levenshtein distance as a sequence-level robustness check is a sensible addition. The prefix-level analysis is a nice touch too.

The soft spots are serious, though. Section 3 defines the two KPIs as differences between original traces and traces "reordered according to the RL optimal policy," and Table 3 reports 31% resource savings and 23% time-span savings from replaying 15,000 traces. The paper never specifies that replay simulation: no transition function, no duration model, no approval-outcome model, no handling of the up-to-10 revisions mentioned in the process description. Without that, the central quantitative claim is not reproducible. The Q-value improvement is partly circular: the augmentation is advertised as selectively increasing the average estimated Q-values, and those higher Q-values are then reported as evidence of success. Equation 4 is malformed; it doesn't parse as a logical constraint. And the conclusion says the method combines offline pre-training with online fine-tuning, while Section 2.3 explicitly says the fine-tuning happens entirely in the offline setting and is fully compliant with an offline RL paradigm. That is a direct contradiction, not a stylistic slip.

Who should read it: researchers in prescriptive process monitoring who want to see the combination of reward shaping and augmentation tried on public benchmarks. They should treat the 31%/23% numbers as unverified until the simulator is released. The Damerau-Levenshtein results are a useful comparison, but they don't measure resource time or process span.

For peer review: I'd send it to referees, but with the expectation of major revision. The idea is salvageable, and the flaws are fixable — but only if the authors either specify the counterfactual simulator in detail or drop the KPI claims and reframe around the sequence-similarity validation.

Referee Report

4 major / 6 minor

Summary. The paper proposes FORLAPS, a five-step prescriptive process monitoring framework that combines offline reinforcement learning with a state-dependent (length-aware) reward shaping mechanism and a process-aware data augmentation technique. The authors apply the framework to a real-life event log from a Canadian energy regulator and report 31% savings in resource time spent and 23% reduction in process time span, comparing favorably against LSTM and Permutation Feature Importance baselines. They also report improved Q-value convergence on nine public datasets and validate the recommended sequences using Damerau-Levenshtein distance at both trace and prefix levels. The central claim is that FORLAPS reliably identifies optimal execution paths and provides context-sensitive next-activity recommendations without online environment interaction.

Significance. If the empirical claims were substantiated, the paper would offer a practical, data-driven approach to prescriptive process monitoring in data-scarce, high-stakes settings, and the integration of length-aware reward shaping with process-aware augmentation is a sensible direction. The authors provide source code on GitHub and evaluate on a diverse set of datasets (Sepsis, BPIC 2015/2017/2019, Traffic Fines), which is a strength. However, the headline results rest on an unspecified replay simulator, and the Q-value improvements are partly circular because the augmentation is described as selectively increasing Q-values. As written, the contribution is not fully established, and the evaluation protocol needs substantial clarification before the claims can be accepted.

major comments (4)

[Section 3, Table 3] The replay simulation that produces the 31% resource-time and 23% time-span savings is not defined. The text says "we replayed 15,000 traces from the test set of event logs using the learned optimal policy," but no transition function, duration model, activity outcome (approval/disapproval) model, or cancellation/revision semantics are specified. The process in Section 2.1 is described as having up to 10 revisions and partially parallel activities, and Section 3 itself concedes that sequential reordering may add delays. Without an explicit simulator, the KPI computations are not reproducible and the comparison against LSTM and PFI baselines is uninterpretable. This is the core empirical claim, so the paper must provide a precise replay model and re-run the evaluation.
[Sections 2.3, 3, and 4.2] Reporting Q-value increases as performance improvements is circular. The abstract and Section 2.3 state that the augmentation technique "selectively increases the average estimated Q-values in sampled batches," and Section 3 then reports a 44% Q-value improvement during fine-tuning, while Section 4.2 reports "outperformed the Offline RL agent by 28 percentage points on the Sepsis dataset and 33 percentage points on the BPIC_2017 dataset" as superiority. If the augmentation is intentionally designed to raise Q-values, higher Q-values are not independent evidence of a better policy. The paper should use externally defined metrics (e.g., expected return under a fixed simulator, success rate, or downstream KPIs) to compare policies, and should either drop the Q-value-based superiority claims or present them only as convergence diagnostics.
[Section 2.3, Eq. (4)] Equation (4) is malformed: it uses the subset symbol "⊆" for trace membership, mixes universal and existential quantifiers with an unclear implication scope, and does not define the validity predicate V(σ', b) that it claims to formalize. As written, the equation cannot be implemented or checked, yet it is the formal basis for the "process semantic check" that the augmentation is said to satisfy. This needs to be rewritten as a precise logical condition over traces, business rules, and the augmented log.
[Section 5 vs. Section 2.3] The paper contradicts itself on whether fine-tuning is offline or online. Section 2.3 says "we enhance policy learning through a fine-tuning phase conducted entirely in the offline setting" and the method is "fully compliant with an offline RL paradigm," but the Conclusion (Section 5) describes FORLAPS as "a hybrid technique that combines offline pre-training with online fine-tuning." This is not a minor wording issue: it changes the nature of the method and its claimed applicability to settings where online interaction is expensive or infeasible. The authors must unambiguously state the operating mode and ensure all sections are consistent.

minor comments (6)

[Section 3, Table 3] The column header "Opt%" is never defined, and the table reports a "96% confidence interval," which is unusual; please define the statistic and justify the confidence level.
[Section 1, Introduction] The introductory paragraph contains a duplicated discussion of manufacturing environments: "Manufacturing environments face similar challenges..." is followed by "Similarly, manufacturing environments rely on well-ordered production activities..." Please remove the redundancy.
[Section 4.1] The phrase "80'%" contains a stray apostrophe and should be "80%."
[Section 4.3] The sentence "BPIC_2019 showed that LSTMs lightly outperformed offline RL" likely means "slightly outperformed"; please correct the typo.
[Section 2.3, Eq. (5)] Equation (5) is not a correct optimal-policy equation: it mixes an argmax over actions with an argmax over policies and omits the expectation over transitions. Please rewrite it using standard notation.
[Section 2.2, Eq. (2) and Algorithm 1] The reward function is written as $-r|s_t|\mathcal{I}(s_t,a_t)$ in Eq. (2) but as $reward \leftarrow -r \cdot |s_t| \cdot \mathcal{I}(s_t,a_t)$ in Algorithm 1; please align the notation and clarify whether the indicator function depends on both state and action or on the outcome only.

Circularity Check

1 steps flagged · score 6.0 of 10

Augmentation success is reported with the same Q-values the augmentation is designed to increase, making the 44% and 19-33 percentage-point 'performance' gains circular; the KPI comparison is independent but underspecified.

self definitional [Abstract; Section 2.3 (Process data augmentation); Section 3 (fine-tuning results); Section 4.2 (Results and discussion)]
"To further enhance learning, we introduce an innovative process-aware data augmentation technique that selectively increases the average estimated Q-values in sampled batches... The observed improvement in policy quality corresponds to higher estimated Q-values, providing empirical evidence that the model’s value estimation reliably converges as training proceeds... Overall, the fine-tuning process yields a 44% improvement in performance... [Section 4.2:] it outperformed the Offline RL agent by 28 percentage points on the Sepsis dataset and 33 percentage points on the BPIC_2017 dataset."

The augmentation is introduced as a procedure that selectively increases average estimated Q-values, and the same estimated Q-values are then used as the success metric: Figure 2 tracks mean Q-values, Section 3 reports a 44% 'improvement in performance' from fine-tuning, and Section 4.2 reports 19-33 percentage-point gains from the Q-value curves in Figures 5-6. Because the manipulated quantity is also the reported outcome, these gains are expected by construction and are not independent evidence of better process prescriptions. The Damerau-Levenshtein comparisons are a separate, non-circular check, but the headline augmentation-related performance numbers reduce to the augmentation's own objective.

full rationale

The paper's strongest formal circularity is in the evaluation of the augmentation/fine-tuning component: the augmentation's stated purpose is to selectively increase average estimated Q-values, and the paper's 'performance' gains (44%, 19-33 percentage points) are measured as increases in those same Q-values. This is a by-construction reduction and warrants a score of 6. The 31%/23% KPI savings on the energy-regulator case study are not formally circular, but they depend on an unspecified replay/simulation of reordered traces; I treat that as reproducibility/correctness risk, not circularity. The Damerau-Levenshtein validation against LSTM and offline-RL baselines is an external metric and gives some independent support to the general claim of improved sequence recommendation. The self-citations (Abbasi et al. 2024; Khadivi et al. 2023/2024) are background references and are not load-bearing, so they do not increase the score. Overall, the central contribution is not wholly defined by its inputs, but a prominent set of performance claims does reduce to the Q-value objective.

Assumptions & free parameters 6 free parameters · 6 assumptions · 0 invented entities

The framework introduces no new physical or ontological entities; its components are algorithmic (reward shaping, augmentation, fine-tuning). The central claim rests mainly on fitted parameters (r, alpha, gamma, augmentation scales) and on domain assumptions about MDP modeling, replay validity, and semantic validity of augmented traces.

free parameters (6)

base reward penalty r = not reported
Constant in Eq 2; scales all penalties and affects the learned policy; chosen by hand or hyperparameter search.
learning rate alpha = not reported
Q-learning step size; tuned via 'comprehensive hyperparameter search' in Section 3.
discount factor gamma = not reported
Q-learning discount; tuned via hyperparameter search in Section 3.
timestamp noise epsilon = uniform[-0.1, 0.1]
Augmentation noise range in Eq 3, chosen by hand without stated justification.
augmentation scale = 100K and 200K timesteps
Number of augmented timesteps used in fine-tuning; chosen without stated criterion in Section 3.
activity removal fraction = 5% of completed groups
Augmentation parameter for removing groups/activities in Section 2.3; chosen by hand.

assumptions (6)

domain assumption Event log traces can be modeled as an MDP with state = executed activity sequence and deterministic transitions.
Section 2.2 defines states as sequences of actions and ignores parallel execution and stochastic outcomes; activity statuses are treated as fixed.
domain assumption The historical event log is a valid behavior policy for offline Q-learning with no unobserved confounding or selection bias.
Q-learning updates use only observed state-action tuples; no coverage or distribution-shift correction beyond augmentation is provided.
ad hoc to paper Reordering activities according to the learned policy does not change activity outcomes or durations; savings arise because early disapproval can skip later work.
KPI definitions in Section 3 compare original traces with reordered versions; this assumption is not tested or modeled explicitly.
ad hoc to paper The length-aware penalty -r|s_t| I(violation) aligns with true process cost and preserves the intended optimal policy.
Eq 2 is presented as reward shaping, but no potential-based shaping argument is given, so the modified MDP's optimal policy is not justified.
ad hoc to paper Augmented traces generated by timestamp noise, duplication, and removal satisfy business constraints, as per Eq 4.
Eq 4 is not well-formed, so compliance is asserted rather than demonstrated.
standard math Q-learning with tabular value iteration converges on these event logs.
Bellman updates in Eq 1 are standard, but offline data coverage and convergence are not analyzed.

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Cite this review

Pith. "Pith review of An Innovative Data-Driven and Adaptive Reinforcement Learning Approach for Context-Aware Prescriptive Process Monitoring." pith.science (2026). https://pith.science/paper/4WHJ2FYV

@misc{pith2026250110543,
  author       = {Pith},
  title        = {Pith review of: An Innovative Data-Driven and Adaptive Reinforcement Learning Approach for Context-Aware Prescriptive Process Monitoring},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4WHJ2FYV}},
  note         = {Machine review of arXiv:2501.10543}
}

read the original abstract

The application of artificial intelligence and machine learning in business process management has advanced significantly, however, the full potential of these technologies remains largely unexplored, primarily due to challenges related to data quality and availability. We present a novel framework called Fine-Tuned Offline Reinforcement Learning Augmented Process Sequence Optimization (FORLAPS), which aims to identify optimal execution paths in business processes by leveraging reinforcement learning enhanced with a state-dependent reward shaping mechanism, thereby enabling context-sensitive prescriptions. Additionally, to compare FORLAPS with the existing models (Permutation Feature Importance and multi-task Long Short Term Memory model), we experimented to evaluate its effectiveness in terms of resource savings and process time reduction. The experimental results on real-life event logs validate that FORLAPS achieves 31% savings in resource time spent and a 23% reduction in process time span. To further enhance learning, we introduce an innovative process-aware data augmentation technique that selectively increases the average estimated Q-values in sampled batches, enabling automatic fine-tuning of the reinforcement learning model. Robustness was assessed through both prefix-level and trace-level evaluations, using the Damerau-Levenshtein distance as the primary metric. Finally, the model's adaptability across industries was further validated through diverse case studies, including healthcare treatment pathways, financial services workflows, permit applications from regulatory bodies, and operations management. In each domain, the proposed model demonstrated exceptional performance, outperforming existing state-of-the-art approaches in prescriptive decision-making, demonstrating its capability to prescribe optimal next steps and predict the best next activities within a process trace.

Figures

Figures reproduced from arXiv: 2501.10543 by the authors.

**Figure 1.** FORLAPS framework (Icons from www.flaticon.com) statuses, and resource allocation records, enabling fine-grained analysis of performance across both categorical and continuous process indicators. We structured our experimental evaluation into two complementary phases: first, a comparative performance assessment against established approaches (Permutation Feature Importance and multitask LSTM models), measuring reso… view at source ↗

**Figure 2.** d confirms that fine-tuning not only preserves the performance of the pre-trained model but also enhances it over time. Importantly, [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

**Figure 3.** LSTM and PFI training results Another model to compare our model’s results with the existing approach, we used Permutation Feature Importance (PFI), a method in explainable AI (XAI), to identify the significance of activity locations. In this context, we observe the location of the studied itemset. When permuting itemsets with multiple activities, a random shuffle may change the relative order of activities in the i… view at source ↗

Figures from the paper (5 more)

**Figure 4.** Figure 4: Training and validation loss using multi-task LSTM over epochs. The Q-value progression for the offline RL method is presented in [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]

**Figure 5.** Figure 5: Q-value history across all datasets using offline RL [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]

**Figure 6.** Figure 6: Q-value history across all datasets using FORLAPS. Offline Q-learning with fine-tuning demonstrated significantly superior performance compared to counterparts. Notably, it outperformed the Offline RL agent by 28 percentage points on the Sepsis dataset and 33 percentag…

**Figure 7.** Figure 7: Mean Damerau–Levenshtein distance with 95% confidence intervals for recommended paths corresponding to desired and undesired outcomes across test set traces. We observed that augmentations affect the learning performance of different datasets in varying ways. For BPIC_…

**Figure 8.** Figure 8: Mean Damerau–Levenshtein distance with 95% confidence intervals for desired outcomes across test set traces at varying prefix lengths. One of the key challenges in these works is the development of generative models based on the environment and outcomes or probabilisti…

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

SCOPE: Sequential Causal Optimization of Process Interventions
cs.LG 2025-12 conditional novelty 5.0 of 10

SCOPE plans sequential intervention decisions in business processes by estimating outcomes with causal models, and generally beats two baselines on two simulated datasets.

Reference graph

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