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Recognizing intentions, interactions, and causes of plan failures

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Abstract

Natural language systems for the description of image sequences have been developed (e.g. Neumann and Novak, 1986; Herzog et al., 1989). Even though these systems were used to verbalize the behaviour of human agents, they were limited in that they could only describe the purely visual, i.e. spatio-temporal, properties of the behaviour observed. For many applications of such systems (e.g. co-driver systems in traffic, expert systems in high-performance sports, tutorial systems that give “apprentices” instructions in construction tasks, etc.), it seems useful to extend their capabilities to cover a greater part of the performance of a human observer and thus make the system more helpful to the user. In particular, an interpretation process ought to be modelled that yields hypotheses about intentional entities from spatio-temporal information about agents. Its results should be verbalized in natural language.

This article presents an integrated approach for the recognition and natural language description of plans, intentions, interactions between multiple agents, plan failures, and causes of plan failures. The system described takes observations from image sequences as input. This type of input poses specific problems for the recognition process. After moving objects have been extracted from the image sequences by a vision system and spatio-temporal entities (such as spatial relations and events) have been recognized by an event-recognition system, a focussing process selects interesting agents to be concentrated on in the plan-recognition process. The set of plan hypotheses can be reduced by a hypothesis-selection component. Plan recognition serves as the basis for intention recognition, interaction recognition, and plan-failure analysis. The recognized intentional entities are described in natural language. The system is designed to extend the range of capabilities of the system SOCCER, which verbalizes real-world image sequences of soccer games in natural language.

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  1. Dept. of Computer Science, University of Hamburg, Bodenstedtstrasse 16, 50, Hamburg, Germany

    Gudula Retz-Schmidt

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The work reported in this article was partially supported by the Special Collaborative Program on Artificial Intelligence and Knowledge-Based Systems (SFB 314) of the German Science Foundation (DFG), project N2: VITRA.

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Gudula, RS. Recognizing intentions, interactions, and causes of plan failures. User Model User-Adap Inter 1, 173–202 (1991). https://doi.org/10.1007/BF00154477

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