Computing Complexity-aware Plans Using Kolmogorov Complexity

Stefansson, Elis; Johansson, Karl Henrik

doi:10.1109/cdc45484.2021.9683287

Cited by 3 publications

(2 citation statements)

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“…InfoRL and related approaches based on mutual information, free energy or similar measures have been widely investigated in theoretical studies and to address simple decision or control settings [19,[23][24][25][26]45,60,[65][66][67][68][69][70][71][72][73][74], but not to study more complex behaviors such as wayfinding as done here. One of the reasons is that formalizing (default) policies for planning can be challenging.…”

Section: Plos Computational Biologymentioning

confidence: 99%

Humans account for cognitive costs when finding shortcuts: An information-theoretic analysis of navigation

et al. 2023

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When faced with navigating back somewhere we have been before we might either retrace our steps or seek a shorter path. Both choices have costs. Here, we ask whether it is possible to characterize formally the choice of navigational plans as a bounded rational process that trades off the quality of the plan (e.g., its length) and the cognitive cost required to find and implement it. We analyze the navigation strategies of two groups of people that are firstly trained to follow a "default policy" taking a route in a virtual maze and then asked to navigate to various known goal destinations, either in the way they want ("Go To Goal") or by taking novel shortcuts ("Take Shortcut"). We address these wayfinding problems using InfoRL: an information-theoretic approach that formalizes the cognitive cost of devising a navigational plan, as the informational cost to deviate from a well-learned route (the "default policy"). In InfoRL, optimality refers to finding the best trade-off between route length and the amount of control information required to find it. We report five main findings. First, the navigational strategies automatically identified by InfoRL correspond closely to different routes (optimal or suboptimal) in the virtual reality map, which were annotated by hand in previous research. Second, people deliberate more in places where the value of investing cognitive resources (i.e., relevant goal information) is greater. Third, compared to the group of people who receive the "Go To Goal" instruction, those who receive the "Take Shortcut" instruction find shorter but less optimal solutions, reflecting the intrinsic difficulty of finding optimal shortcuts. Fourth, those who receive the "Go To Goal" instruction modulate flexibly their cognitive resources, depending on the benefits of finding the shortcut. Finally, we found a surprising amount of variability in the choice of navigational strategies and resource investment across participants. Taken together, these results illustrate the benefits of using InfoRL to address navigational planning problems from a bounded rational perspective.

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Section: Plos Computational Biologymentioning

confidence: 99%

Humans account for cognitive costs when finding shortcuts: An information-theoretic analysis of navigation

et al. 2023

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show abstract

“…InfoRL and related approaches based on mutual information, free energy or similar measures have been widely investigated in theoretical studies and to address simple decision or control settings (19,(23)(24)(25)(26)44,54,(58)(59)(60)(61)(62)(63)(64)(65)(66)(67), but not to study more complex behaviors such as wayfinding as done here. One of the reasons is that formalizing (default) policies for planning can be challenging.…”

Section: Discussionmentioning

confidence: 99%

Humans account for cognitive costs when finding shortcuts: An information-theoretic analysis of navigation

Lancia

Eluchans

D’Alessandro

et al. 2022

Preprint

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When faced with navigating back somewhere we have been before we might either retrace our steps or seek a shorter path. Both choices have costs. In this study, we ask whether it is possible to characterize formally the choice of navigational plans in terms of a bounded rational process that trades off the quality of the plan (e.g., its length) and the cognitive cost required to find and implement it. We analyze the navigation strategies of two groups of people that are firstly trained to follow a "default policy" taking a route in a virtual maze and then asked to navigate to various known goal destinations, either in the way they want ("Go To Goal") or by taking novel shortcuts ("Take Shortcut"). We address these wayfinding problems using InfoRL: an information-theoretic approach that formalizes the cognitive cost of devising a navigational plan, as the informational cost to deviate from a well-learned route (the "default policy"). In InfoRL, optimality does not refer to finding the shortest route, but to the best trade-off between route length and the amount of control information required to find it. We report four main findings. First, the navigational strategies automatically identified by InfoRL correspond closely to different routes (optimal or suboptimal) in the virtual reality map, which were annotated by hand in previous research. Second, people deliberate more in places where the value of investing cognitive resources (i.e., relevant goal information) is greater. Third, the group of people who receive the "Take Shortcut" find shorter solutions compared to those who receive the "Go To Goal" instruction. However, while the former appear to be naively more efficient if one only considers plan length, our bounded rational approach reveals that they invest more cognitive resources and find overall less optimal solutions, reflecting the intrinsic difficulty of finding optimal shortcuts. While prior analysis indicates greater male optimality in short-cut finding, we find that both male and female participants similarly modulate their cognitive resources in a task-dependent way, as formally expressed through our information-theoretic approach. Finally, we found a surprising amount of variability in the choice of navigational strategies and resource investment across trials. Participants do not simply adopt one strategy and maintain it. Taken together, these results illustrate the benefits of using InfoRL to address navigational planning problems from a bounded rational perspective.

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