=Paper=
{{Paper
|id=Vol-3299/Paper04
|storemode=property
|title=Process Mining-Based Goal Recognition (Extended Abstract)
|pdfUrl=https://ceur-ws.org/Vol-3299/Paper04.pdf
|volume=Vol-3299
|authors=Zihang Su
|dblpUrl=https://dblp.org/rec/conf/icpm/Su22
}}
==Process Mining-Based Goal Recognition (Extended Abstract)==
https://ceur-ws.org/Vol-3299/Paper04.pdf
Process Mining-Based Goal Recognition (Extended
Abstract)⋆
Zihang Su1,*,†
1
The University of Melbourne
Abstract
The process mining-based goal recognition system can infer the intention of an autonomous agent based
on its historical behaviors. The developed system can learn process models using process discovery
techniques and then use conformance diagnostics between the constructed process models and a new
observation to formulate a probability distribution over a range of possible goals the agent attempts
to achieve. Compared to other state-of-the-art goal recognition approaches, the proposed approach
does not rely on handcrafted domain knowledge and, thus, is applicable in many real-world scenarios.
Combined with re-learn mechanism and process model repair techniques, the developed system can
continuously solve goal recognition problems and is robust to environmental changes.
Keywords
goal recognition, process mining, environmental change, process model repair
1. Introduction
Goal Recognition (GR) techniques aim to infer the intentions of an autonomous agent according
to the observed actions of that agent [1]. GR techniques play an important role in many areas,
such as the support of adversarial reasoning [2], trajectory prediction [3], and human-computer
collaboration [4].
Three concepts are inherent to the understanding of GR: A plan is a sequence of actions that
were or should be taken to achieve a goal; An agent, such as a robot or human, follows plans
to accomplish goals; A GR system is a software that implements a GR technique capable of
inferring the goals of agents based on partial knowledge about the plans (partially observed
plans). When a GR system analyzes actions executed by an agent, it aims to forecast the full
plan that the agent is following and, hence, the goal that will be achieved after completing the
plan.
The existing GR techniques can be classified into two main categories: 1) Observations of an
agent’s actions are “matched” to a plan (the one judged to being carried out by the agent) in a
pre-defined library encoding the standard operational procedures of the domain [1], namely,
plan library-based approaches; 2) Appealing to the principle of rational behavior, an agent
is assumed to be taking the “optimal” path to the goal: the more rational a behavior appears
ICPM 2022 Doctoral Consortium and Tool Demonstration Track
*
Corresponding author.
" zihangs@student.unimelb.edu.au (Z. Su)
0000-0003-1039-7462 (Z. Su)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
18
towards a goal, the more likely such goal is the agent’s goal. Ramirez and Geffner [5] have
sparked a plethora of approaches not needing any a priori set of plans. These approaches
perform GR by exploiting planning techniques to automatically generate plans relative to a
domain theory, namely, planning-based approaches.
The challenge for plan library-based approaches is in obtaining a set of standard plans and
hand-coding an informative model (plan library) that can represent the standard plans. Besides,
these approaches do not accommodate uncertainty (cannot generalize to the observations that
are not pre-stored in the plan library). For planning-based approaches, even though specifying
domain models could be less effort demanding than hand-coding plan libraries, and “new”
plans can be found, acquiring domain models is far from trivial due to the difficulty of defining
models using standard declarative languages [6]. It is especially challenging to acquire domain
models of real-world environments. Hence, planning-based approaches are difficult to apply in
real-world scenarios.
2. Proposed Solutions
RQ1 is solved and verified with a published paper [7], explained in 2.1, while RQ2 is in progress
[8], and two possible solutions are explained in 2.2
2.1. Answer to RQ1
We proposed a GR approach based on process mining techniques, namely, the process mining
(PM-)based approach, to learn knowledge (models) by observing agents’ behaviors, then the
PM-based GR approach can recognize agents’ goals according to newly observed behaviors and
the learned knowledge [7]. In this approach, firstly, we collect the previous sequences of actions
executed by the agent, and the action sequences for achieving the same goals are grouped and
converted to event logs; thus, multiple event logs are obtained, and each event log records the
plans for achieving a particular goal. Secondly, we apply the process discovery technique [9, 10]
to mine Petri nets (to represent the knowledge models) from the obtained event logs. Thirdly,
we apply conformance checking techniques [11] to measure the costs of alignments between
the newly observed action sequence and the learned models (Petri nets). Finally, we construct a
probability distribution over all goal candidates according to the costs of alignments.
2.2. Answer to RQ2
One of the possible solutions for RQ2 is to re-learn the knowledge model (re-mine Petri nets)
based on the behaviors of the agent acting in the new environment. As we assume the full
model of the underlying environment is unknown, the environmental change can not be directly
observed. Therefore, we need to construct the mechanism for deleting the environmental
change and deciding when to re-learn from which observed dataset. The recognition accuracy
score can be an indicator of environmental changes (if the underlying environment changes,
the agent may act differently in the new environment, which causes the accuracy score to drop).
The dataset used for re-learning knowledge models is the feedback from each “single-shot” GR
task. Another possible solution is to apply the process model repair techniques [12, 10] for
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adjusting the knowledge models (Petri-nets) when the GR system receives negative feedback
(the inferred goal is not the ground truth). A challenge for applying the process model repair
technique is to tackle the problem of deleting a behavior from a knowledge model.
3. Evaluation Methodology
We compared the PM-based GR approach with other state-of-the-art GR approaches [5, 13] using
the planning domains1 as dataset. The result shows that, compared to other approaches, the PM-
based GR approach has a similar GR performance in terms of recall and a faster response time. We
conducted GR experiments on three real-world domains which obtained from three BPI challenge
logs, namely activities of daily living,2 building permit applications,3 and environmental permit
applications.4 The results verified that the PM-based GR approach is applicable in real-world
scenarios; for details refer to [7]. For RQ2, since continuous GR over changing environments is a
new problem, commonly used experimental datasets and benchmarks are not available. Thus, we
built a simulator [8] that can generate experimental data based on classical planning domains.1
We will conduct experiments on the generated datasets to evaluate the GR performance before
and after the environmental changes. We will compare different re-learn mechanisms and model
repair techniques to study if there exists an optimal solution for all scenarios.
4. Conclusion
This research project utilizes the existing process mining-related techniques [9, 10, 12, 11] to
implement a GR approach that can automatically learn the knowledge model, and overcomes the
limitations of for the state-of-the-art GR techniques [1, 5, 13]. Additionally, the research aims
to provide a possible solution to the problem of GR in non-stationary environments [14, 15].
Acknowledgments
Thanks to my supervisors, Dr. Artem Polyvyanyy, Dr. Nir Lipovetzky, Prof. Sebastian Sardina,
and Dr. Nick van Beest. Thanks to the funding institutions, the University of Melbourne,
Chinese Scholarship Council, and Data61 CSIRO.
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