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Practical Reasoning About Complex Activities

Part of the Lecture Notes in Computer Science book series (LNAI,volume 10349)

Abstract

In this paper, we present an argument-based mechanism to generate hypotheses about belief-desire-intentions on dynamic and complex activities of a software agent. We propose to use a composed structure called activity as unit for agent deliberation analysis, maintaining actions, goals and observations of the world always situated into a context. Activity transformation produces changes in the knowledge base activity structure as well in the agent’s mental states. For example, in car driving as a changing activity, experienced and novice drivers have a different mental attitudes defining distinct deliberation processes with the same observations of the world. Using a framework for understanding activities in social sciences, we endow a software agent with the ability of deliberate, drawing conclusion about current and past events dealing with activity transformations. An argument-based deliberation is proposed which progressively reason about activity segments in a bottom-up manner. Activities are captured as extended logic programs and hypotheses are built using an answer-set programming approach. We present algorithms and an early-stage implementation of our argument-based deliberation process.

Keywords

  • Practical reasoning
  • Agents
  • Complex activity
  • Argumentation
  • Deliberation
  • Tool

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Fig. 1.

Adapted from [15]

Fig. 2.

Notes

  1. 1.

    Not only human activity but activity of any subject.

  2. 2.

    A general perspective about argumentation theory is presented in [4].

  3. 3.

    Semantic in terms of a semantic system [23]. A semantic system relates a set F of logical formulae to a set M of formal models, each representing a conceivable state of the world in enough detail to determine when a given formula represents a true assertion in that state of the world.

  4. 4.

    Some actions and operations are based on a self-driving vehicle example in [22].

  5. 5.

    Please, note that in atom: \( speed>0kmh^{co} \) the symbol > does not belong to the underlying language, it is a semantic interpretation of a world observation.

  6. 6.

    Assuming that \( AF_{op} = \langle \mathcal {H}_{op}, Att_{op} \rangle \) is the resulting argumentation framework obtained from R and \(SEM(AF_{op}) = \{Ext_1, \dots , Ext_m \}, (m \geqslant 1)\) is the set of extensions suggested by an argumentation semantics SEM.

  7. 7.

    Similarly Definition 6, assuming that \( AF_{obj} = \langle \mathcal {H}_{obj}, Att_{obj} \rangle \) is the resulting argumentation framework obtained from \( R^{'}\) and \(SEM(AF_{obj}) = \{Ext_1, \dots , Ext_m \}, (m \geqslant 1)\) is the set of extensions suggested by an argumentation semantics SEM.

  8. 8.

    Sources and manual instructions of the tool can be download in: https://github.com/esteban-g/recursive_deliberation.

  9. 9.

    e.g. the so called, “potential desires” and “potential initial goals” in [1, 2].

  10. 10.

    In [1] Definition 4 it is state that “Note that each desire is a sub-desire of itself”.

  11. 11.

    In this paper we do not address automatization, this particular topic is being currently explored by the authors.

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Guerrero, E., Lindgren, H. (2017). Practical Reasoning About Complex Activities. In: Demazeau, Y., Davidsson, P., Bajo, J., Vale, Z. (eds) Advances in Practical Applications of Cyber-Physical Multi-Agent Systems: The PAAMS Collection. PAAMS 2017. Lecture Notes in Computer Science(), vol 10349. Springer, Cham. https://doi.org/10.1007/978-3-319-59930-4_7

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  • DOI: https://doi.org/10.1007/978-3-319-59930-4_7

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