Towards Neural Network based Cognitive Models of Dynamic Decision-Making by Humans

Modelling human cognitive processes in dynamic decision-making tasks has been an endeavor in AI for a long time. Some initial works have attempted to utilize neural networks (and large language models) but often assume one common model for all humans and aim to emulate human behavior in aggregate. However, behavior of each human is distinct, heterogeneous and relies on specific past experiences in specific tasks. To that end, we build on a well known model of cognition, namely Instance Based Learning (IBL), that posits that decisions are made based on similar situations encountered in the past. We propose two new attention based neural network models to model human decision-making in dynamic settings. We experiment with two distinct datasets gathered from human subject experiment data, one focusing on detection of phishing email by humans and another where humans act as attackers in a cybersecurity setting and decide on an attack option. We conduct extensive experiments with our two neural network models, IBL, and GPT3.5, and demonstrate that one of our neural network models achieves the best performance in representing human decision-making. We find an interesting trend that all models predict a human's decision better if that human is better at the task. We also explore explanation of human decisions based on what our model considers important in prediction. Overall, our work yields promising results for further use of neural networks in cognitive modelling of human decision making. Our code is available at https://github.com/shshnkreddy/NCM-HDM.

Preserving the Privacy of Reward Functions in MDPs through Deception

Preserving the privacy of preferences (or rewards) of a sequential decision-making agent when decisions are observable is crucial in many physical and cybersecurity domains. For instance, in wildlife monitoring, agents must allocate patrolling resources without revealing animal locations to poachers. This paper addresses privacy preservation in planning over a sequence of actions in MDPs, where the reward function represents the preference structure to be protected. Observers can use Inverse RL (IRL) to learn these preferences, making this a challenging task. Current research on differential privacy in reward functions fails to ensure guarantee on the minimum expected reward and offers theoretical guarantees that are inadequate against IRL-based observers. To bridge this gap, we propose a novel approach rooted in the theory of deception. Deception includes two models: dissimulation (hiding the truth) and simulation (showing the wrong). Our first contribution theoretically demonstrates significant privacy leaks in existing dissimulation-based methods. Our second contribution is a novel RL-based planning algorithm that uses simulation to effectively address these privacy concerns while ensuring a guarantee on the expected reward. Experiments on multiple benchmark problems show that our approach outperforms previous methods in preserving reward function privacy.

Safety through feedback in Constrained RL

In safety-critical RL settings, the inclusion of an additional cost function is often favoured over the arduous task of modifying the reward function to ensure the agent's safe behaviour. However, designing or evaluating such a cost function can be prohibitively expensive. For instance, in the domain of self-driving, designing a cost function that encompasses all unsafe behaviours (e.g. aggressive lane changes) is inherently complex. In such scenarios, the cost function can be learned from feedback collected offline in between training rounds. This feedback can be system generated or elicited from a human observing the training process. Previous approaches have not been able to scale to complex environments and are constrained to receiving feedback at the state level which can be expensive to collect. To this end, we introduce an approach that scales to more complex domains and extends to beyond state-level feedback, thus, reducing the burden on the evaluator. Inferring the cost function in such settings poses challenges, particularly in assigning credit to individual states based on trajectory-level feedback. To address this, we propose a surrogate objective that transforms the problem into a state-level supervised classification task with noisy labels, which can be solved efficiently. Additionally, it is often infeasible to collect feedback on every trajectory generated by the agent, hence, two fundamental questions arise: (1) Which trajectories should be presented to the human? and (2) How many trajectories are necessary for effective learning? To address these questions, we introduce \textit{novelty-based sampling} that selectively involves the evaluator only when the the agent encounters a \textit{novel} trajectory. We showcase the efficiency of our method through experimentation on several benchmark Safety Gymnasium environments and realistic self-driving scenarios.

A Robust Classification Framework for Byzantine-Resilient Stochastic Gradient Descent

This paper proposes a Robust Gradient Classification Framework (RGCF) for Byzantine fault tolerance in distributed stochastic gradient descent. The framework consists of a pattern recognition filter which we train to be able to classify individual gradients as Byzantine by using their direction alone. This filter is robust to an arbitrary number of Byzantine workers for convex as well as non-convex optimisation settings, which is a significant improvement on the prior work that is robust to Byzantine faults only when up to 50% of the workers are Byzantine. This solution does not require an estimate of the number of Byzantine workers; its running time is not dependent on the number of workers and can scale up to training instances with a large number of workers without a loss in performance. We validate our solution by training convolutional neural networks on the MNIST dataset in the presence of Byzantine workers.