The Challenge of Credit Assignment in Decision-Making

As technology advances, creators are trying to make machines that act more like humans, especially when it comes to making decisions. One key area of interest is how actions relate to outcomes over time, known as "Credit Assignment." This is important because it helps both people and AI learn from their experiences.

#The Credit Assignment Problem

When we make a series of choices, we often only see the result at the end. For instance, in a game like chess, you only discover if you've won or lost at the end of the game. This makes it hard to figure out which moves were good or bad. This scenario illustrates the credit assignment problem: how do we figure out which actions led to which outcomes?

This issue is not just a challenge for people; AI systems also struggle with it. Some approaches aim to solve this problem, with one popular method being Temporal Difference (TD) learning. This method allows AI to estimate the value of decisions without seeing the final results right away. However, it's uncertain if these methods truly mimic how humans learn from delayed feedback.

#The Need for Cognitive Models

Cognitive models are designed to simulate how people make decisions and solve problems. They can help researchers understand how humans tackle credit assignment. However, there hasn't been much research focused specifically on how people manage the credit assignment problem in relation to AI.

This study seeks to fill that gap by exploring different credit assignment methods within a cognitive model based on Instance-Based Learning Theory (IBLT). We looked at how these methods affect decision-making in various tasks.

#Goal-Seeking Tasks and Decision Complexity

In our research, we used a task set in a grid environment where participants need to navigate to reach targets while avoiding obstacles. The level of challenge varies based on how complex the decisions are. Some situations make it easier to reach targets quickly, while others require more thought and strategy.

We aimed to find out how different credit assignment methods would perform in these tasks. Specifically, we examined three methods: equal credit, exponential credit, and a new method that combines IBL with TD learning.

#Experimental Setup

To gather data, we conducted two experiments with human participants, using gridworlds of varying complexity. Each participant completed multiple episodes of the same task, aiming to find the highest value target while minimizing steps taken.

In the first experiment, participants had a clear view of the grid, while the second experiment presented them with limited information. This design allowed us to see how information availability affects decision-making.

#Analyzing Human Performance

Our analysis compared human participants' decisions with the outcomes from our AI models. We wanted to see how well our models could replicate human behavior.

In both experiments, we observed that humans are influenced by decision complexity, meaning that when tasks became harder, their performance dropped. Interestingly, while AI models learned quickly and became better at finding targets, they did not always reflect human strategies.

#Results of the First Experiment

In the first experiment, participants performed better when they had more information about the task. They used strategies that reflected a clear understanding of their environment. This was particularly true in simpler conditions, where they could follow a linear path to their target.

On the other hand, models using equal credit were able to match human performance in finding the highest value targets. However, they struggled with optimal choices that required fewer steps.

#Learning Curves

The learning curves from the first experiment showed that human performance improved over time, but the AI models demonstrated different patterns. For instance, models using TD learning started slowly but grew to outperform human participants later.

#Results of the Second Experiment

In the second experiment, we restricted the information available to participants. This change significantly affected their performance, especially in complex tasks. With limited information, humans struggled to find targets, while the models continued to show consistent performance against their previous tasks.

#Information Impact

Restricting information made decision-making more difficult for humans. As a result, the gap between human and AI performance widened, especially in complex situations. Models like IBL-TD and Q-learning adjusted better to the challenges posed by the task compared to human participants.

#Understanding Redundant Actions

One key finding is that humans tended to avoid redundant actions when they had more information. In contrast, AI models, particularly those using TD methods, exhibited a higher rate of redundancies early on. This reflects a less efficient exploration strategy as they navigated their environments.

#Strategies for Movement

We also observed how humans adopted linear movement strategies, especially in simpler tasks. Models, on the other hand, did not show this tendency initially. However, as they learned over time, they began to align more closely with human behavior.

#Implications for AI Development

Our findings reveal significant differences in how AI and humans learn from feedback in decision-making tasks. The TD methods, while effective in the long run, lag behind in the initial stages of learning when compared to human adaptability.

#Enhancing Human Learning

Though AI models had shortcomings in initial learning phases, they eventually surpassed human performance in complex tasks. This suggests that integrating AI systems into decision-making roles could help enhance human learning and decision-making under uncertainty.

#Future Directions

The challenges highlighted by these experiments present opportunities for future research. Understanding how best to combine human intuition with computational efficiency will be crucial in developing systems that support human decision-making.

In conclusion, while AI has made great strides in mimicking human decision-making, understanding and improving this interaction remains a vital area of focus. Our work points to potential paths for enhancing both human learning and the development of more effective AI agents.