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1. SPNets: Human-like Navigation Behaviors with Uncertain Goals

   https://doi.org/10.1145/3424636.3426911  

   Sohre, Nicholas; Guy, Stephen J. (October 2020, SPNets: Human-like Navigation Behaviors with Uncertain Goals)

   null (Ed.)

   Most path planning techniques use exact, global information of the environment to make optimal or near-optimal plans. In contrast, humans navigate using only local information, which they must augment with their understanding of typical building layouts to guess what lies ahead, while integrating what they have seen already to form mental representations of building structure. Here, we propose Scene Planning Networks (SPNets), a neural network based approach for formulating the long-range navigation problem as a series of local decisions similar to what humans face when navigating. Agents navigating using SPNets build additive neural representations of previous observations to understand local obstacle structure, and use a network-based planning approach to plan the next steps towards a fuzzy goal region. Our approach reproduces several important aspects of human behavior that are not captured by either full global planning or simple local heuristics. 

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2. Heterogeneous Decision Making in Mixed Traffic: Uncertainty-aware Planning and Bounded Rationality

   Wang, Hang; Fang, Qiaoyi; Zhang, Junshan (March 2025, https://cpal.cc/)

   Candes, Emmanuel; Ma, Yi (Ed.)

   The past few years have witnessed a rapid growth of the deployment of automated vehicles (AVs). Clearly, AVs and human-driven vehicles (HVs) will co-exist for many years, and AVs will have to operate around HVs, pedestrians, cyclists, and more, calling for fundamental breakthroughs in AI designed for mixed traffic to achieve mixed autonomy. Thus motivated, we study heterogeneous decision making by AVs and HVs in a mixed traffic environment, aiming to capture the interactions between human and machine decision-making and develop an AI foundation that enables vehicles to operate safely and efficiently. There are a number of challenges to achieve mixed autonomy, including 1) humans drivers make driving decisions with bounded rationality, and it remains open to develop accurate models for HVs' decision making; and 2) uncertainty-aware planning plays a critical role for AVs to take safety maneuvers in response to the human behavior. In this paper, we introduce a formulation of AV-HV interaction, where the HV makes decisions with bounded rationality and the AV employs uncertainty-aware planning based on the prediction on HV's future actions. We conduct a comprehensive analysis on AV and HV's learning regret to answer the questions: 1) How does the learning performance depend on HV's bounded rationality and AV's planning; 2) How do different decision making strategies impact the overall learning performance? Our findings reveal some intriguing phenomena, such as Goodhart's Law in AV's learning performance and compounding effects in HV's decision making process. By examining the dynamics of the regrets, we gain insights into the interplay between human and machine decision making. 

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3. A Quantitative Analysis of Rational Decisions Under Uncertainty in Engineering Systems Design

   https://doi.org/10.1017/pds.2025.10039  

   Mutlu, Kaan; Javadpour, Nikoo; Sha, Zhenghui; Bayrak, Alparslan Emrah (August 2025, Proceedings of the Design Society)

   Rational decision-making is crucial in the later stages of engineering system design to allocate resources efficiently and minimize costs. However, human rationality is bounded by cognitive biases and limitations. Understanding how humans deviate from rationality is critical for guiding designers toward better design outcomes. In this paper, we quantify designer rationality in competitive scenarios based on utility theory. Using an experiment inspired by crowd-sourced contests, we show that designers employ varied search strategies. Some participants approximate a Bayesian agent that aimed to maximize its expected utility. Those with higher rationality reduce uncertainty more effectively. Furthermore, rationality correlates with both the proximity to optimal design and design iteration costs, with winning participants exhibiting greater rationality than losing participants. 

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4. Bounded-Rational Pursuit-Evasion Games

   https://doi.org/10.23919/ACC50511.2021.9483152  

   Guan, Yue; Maity, Dipankar; Kroninger, Christopher M.; Tsiotras, Panagiotis (May 2021, American Control Conference)

   We present a framework that incorporates the principle of bounded rationality into dynamic stochastic pursuit-evasion games. The solution of a stochastic game is generally characterized by its (Nash) equilibria in feedback form, whose calculation may require extensive computational resources. In this paper, the agents are modeled as bounded rational entities with limited computational capabilities. We illustrate the proposed framework by applying it to a pursuit-evasion game between two aerial vehicles in a stochastic wind field. We show how such a game may be discretized and properly analyzed by casting it as an iterative sequence of finite-state Markov Decision Processes (MDPs). Leveraging tools and algorithms from the cognitive hierarchy theory (“level-k thinking”) we compute the solution of the ensuing discrete game, while taking into consideration the rationality level of each agent. We also present an online algorithm for each agent to infer its opponent's rationality level. 

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5. Cognitive steering in deep neural networks via long-range modulatory feedback connections

   Konkle, Talia; Alvarez, George A (December 2023, Proceedings of the 37th International Conference on Neural Information Processing Systems)

   Given the rich visual information available in each glance, humans can internally direct their visual attention to enhance goal-relevant information—a capacity often absent in standard vision models. Here we introduce cognitively and biologically-inspired long-range modulatory pathways to enable 'cognitive steering' in vision models. First, we show that models equipped with these feedback pathways naturally show improved image recognition, adversarial robustness, and increased brain alignment, relative to baseline models. Further, these feedback projections from the final layer of the vision backbone provide a meaningful steering interface, where goals can be specified as vectors in the output space. We show that there are effective ways to steer the model that dramatically improve recognition of categories in composite images of multiple categories, succeeding where baseline feed-forward models without flexible steering fail. And, our multiplicative modulatory motif prevents rampant hallucination of the top-down goal category, dissociating what the model is looking for, from what it is looking at. Thus, these long-range modulatory pathways enable new behavioral capacities for goal-directed visual encoding, offering a flexible communication interface between cognitive and visual systems. 

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