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|title=None
|pdfUrl=https://ceur-ws.org/Vol-2491/abstract70.pdf
|volume=Vol-2491
|dblpUrl=https://dblp.org/rec/conf/bnaic/AyoobiCVV19
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==None==
<pdf width="1500px">https://ceur-ws.org/Vol-2491/abstract70.pdf</pdf>
<pre>
               Handling Unforeseen Failures Using
               Argumentation-Based Learning ? ??

          Hamed Ayoobi1 , Ming Cao2 , Rineke Verbrugge1 , and Bart Verheij1
    1
        Department of Artificial Intelligence, Bernoulli Institute, Faculty of Science and
                   Engineering, University of Groningen, The Netherlands
        2
          Institute of Engineering and Technology (ENTEG), Faculty of Science and
                   Engineering, University of Groningen, The Netherlands

In our paper published as [1], a new argumentation-based learning technique is
proposed to handle unforeseen failures for a robot. The method is in the category
of online incremental learning techniques that uses argumentation theory for
modeling the support and attack relation between arguments in the knowledge
base of the robot, which itself is constructed in an online incremental manner.
    The elderly population is rising in Europe [2] and General-Purpose Service
Robots (GPSR) can assist this fragile group of people in the future. Such robots
should operate in a home-like environment with a dynamic nature where even
the robot’s programmer cannot predict what kind of failure conditions the robot
might confront during its task executions. Unforeseen external failures can oc-
cur because of an unexpected change in the environment around the robot, for
instance, a new type of obstacle blocking the way. It is important to note that
confronting unforeseen failures is mostly the default state for GPSRs, rather
than an exceptional state as often described in the literature.
    Consequently, GPSRs need to efficiently handle unforeseen failure conditions.
Since the robot has never seen these kinds of failures before, it cannot be simply
pre-programmed to handle them. Therefore, the robot should use a learning
mechanism to test different recovery behaviors and figure out how to recover
from each failure state.
    In this research, we have proposed to use a new approach that incorporates
argumentation frameworks for online incremental learning. This method uses a
bipolar argumentation framework [3] to generate a set of related hypotheses out
of the robot’s observations and then models the defeasibility relation between
the generated hypotheses using an abstract argumentation framework [4]. The
support relations in this model are related to the number the observations of
the robot. Whenever the robot observes a state with some specific feature-values
that supports one failure recovery behavior, it adds all the subsets of the feature
values as support nodes to the model. The model gets updated when a new data
instance is observed.
?
   This work is conducted at the centre of Data Science and Systems Complexity
   (DSSC), and sponsored with a Marie Sklodowska-Curie COFUND grant, agreement
   no. 754315.
??
   Copyright c 2019 for this paper by its authors. Use permitted under Creative Com-
   mons License Attribution 4.0 International (CC BY 4.0).
2       H. Ayoobi et al.

    We evaluated the performance of our method by conducting a comparison
with state-of-the-art online incremental learning methods chosen based on the
recent survey [5]. A new robotic scenario has been developed as a case study
to evaluate the performance of the methods. In this scenario, the robot should
enter a room using one of three doors. However, it might confront different types
of obstacles of different colors on its way. Depending on the type and color of
the obstacle, there is only one randomly selected successful recovery behavior
that the robot should detect by learning.
    The result of this experiment (Figure 1) not only showed that the ultimate
classification precision of the proposed method is higher than other methods but
it also emphasized that the learning speed of the argumentation-based learning
approach is higher than other methods. This means that the proposed method
can learn with higher precision and less number of observations.




Fig. 1. The comparison of the Argumentation-Based Learning (ABL method) with key
methods for incremental online learning [5] using the test scenario.

References
1. H. Ayoobi, M. Cao, R. Verbrugge, and B. Verheij, “Handling unforeseen failures
   using argumentation-based learning,” in 2019 IEEE 15th International Conference
   on Automation Science and Engineering (CASE), pp. 1699–1704, Aug 2019.
2. B. Rechel, E. Grundy, J.-M. Robine, J. Cylus, J. P. Mackenbach, C. Knai, and
   M. McKee, “Ageing in the European Union,” The Lancet, vol. 381, no. 9874,
   pp. 1312–1322, 2013.
3. L. Amgoud, C. Cayrol, M.-C. Lagasquie-Schiex, and P. Livet, “On bipolarity in
   argumentation frameworks,” International Journal of Intelligent Systems, vol. 23,
   no. 10, pp. 1062–1093, 2008.
4. P. M. Dung, “On the acceptability of arguments and its fundamental role in non-
   monotonic reasoning, logic programming and n-person games,” Artificial Intelli-
   gence, vol. 77, no. 2, pp. 321–357, 1995.
5. V. Losing, B. Hammer, and H. Wersing, “Incremental on-line learning: A review and
   comparison of state of the art algorithms,” Neurocomputing, vol. 275, pp. 1261–1274,
   2018.

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