Noname manuscript No.
(will be inserted by the editor)
Identifying Inter-Individual Planning Strategies
Rebecca Albrecht · Marco Ragni · Felix
Steffenhagen
Received: date / Accepted: date
Introduction. In cognitive modeling, a computer model based on psychological
assumptions is used to describe human behavior in a certain task. In order to
evaluate the quality of a cognitive model, average results from behavioral experiments, e.g. response times, are compared to average results predicted by a cognitive model. However, this method does not accommodate for modeling qualitative
inter-individual differences.
In this article, we present a method for analyzing qualitative differences in user
strategies with respect to assumptions on psychological factors which are different
in individuals, e.g. working memory capacity. This is done by comparing strategies used by participants in a behavioral experiment with strategies predicted by
cognitive agents controlled by individual factors. As a result, we identify a group
of best-replicating agents for each individual, which can be identified as factors
controlling an individual participants planning strategy.
Method Sketch. In order to represent and analyze planning strategies, we use so
called strategy graphs. For each task tested in a planning domain, a strategy graph
is constructed, which includes all possible paths (strategies) from a designated start
state to a set of designated goal states. An example for a partial strategy graph
with a planning depths of three in a task from the Rush Hour planning domain
(Flake & Baum, 2002) is shown in Figure 1. In order to not represent irrelevant
move transitions, strategy graphs are reduced with respect to properties from the
problem domain, e.g. move transpositions in the chess problem domain.
In order to identify cognitive agents which best replicate participants strategies, different similarity measures are possible. As a result, each participant is
assigned to a set of cognitive agents with a maximal similarity of chosen strategies. Inter-individual psychological factors can be narrowed down by the values of
psychological properties used to control planning behavior of assigned agents.
With respect to the presented method, the quality of the cognitive model, i.e.
the set of cognitive agents replicating individual planning strategies, is calculated
based on the mean similarity over all participants and all cognitive agents.
Address(es) of author(s) should be given
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Rebecca Albrecht et al.
Fig. 1 Example of a partial strategy graph for a planning depth of three in the Rush Hour
problem domain. States are possible Rush Hour board configurations. Each transition represents a move of a participant in a behavioral experiment or a move of a cognitive agent based
on simulation results.
Preliminary Evaluation Results. We evaluated the proposed method preliminarily
in the Rush Hour planning domain (Steffenhagen, Albrecht, & Ragni, 2014). Human data was collected in a psychological experiment with 20 participants solving
22 different tasks. Cognitive agents where programmed to use means end analysis (Faltings & Pu, 1992; Mcdermott, 1996) and different parameter settings to
control local planning behavior with respect to assumed individual factors. As a
similarity measure the Waterman-Smith algorithm for calculating local sequence
alignments was used (Smith & Waterman, 1981). For each of the 20 participants a
set of best replicating agents was identified based on the maximal mean similarity
of humans’ and cognitive agents’ strategies (1) constantly over all tasks and (2)
for each task separately. The evaluation reveals that cognitive agents using means
end analysis and a certain set of parameters can predict 44 % of human strategies
in case (1) and 76 % of human strategies in (2).
References
Faltings, B., & Pu, P. (1992). Applying means-ends analysis to spatial planning.
In Proc. of the 1991 ieee/rsj int. workshop on intelligent robots and systems
(iros 91) (pp. 80–85).
Flake, G. W., & Baum, E. B. (2002). Rush hour is PSPACE-complete, or ”why
you should generously tip parking lot attendants”. Theoretical Computer
Science, 270 , 895–911.
Mcdermott, D. (1996). A heuristic estimator for means-ends analysis in planning.
In Proc. of third int. conf. on ai planning systems aips-96 (pp. 142–149).
Smith, T. F., & Waterman, M. S. (1981). Identification of common molecular
subsequences. Journal of Molecular Biology, 147 , 195–197.
Steffenhagen, F., Albrecht, R., & Ragni, M. (2014). Automatic Identification
of Human Strategies by Cognitive Agents. In Submitted to KI Conference
2014.