Collaborative Decision-Making Dialogues: Challenges for AI Assistants

Core Concepts

AI assistants must collaborate with humans via natural language to help them make complex decisions by combining their complementary abilities.

Abstract

This paper introduces a class of tasks called decision-oriented dialogues, in which AI assistants such as large language models (LMs) must collaborate with one or more humans to help them make complex decisions. The authors formalize three everyday domains where users face decisions: (1) assigning reviewers to conference papers, (2) planning a multi-step travel itinerary, and (3) negotiating group travel plans. In these tasks, AI assistants and users have disparate abilities that they must combine to arrive at the best decision. Assistants can access and process large amounts of information, while users have preferences and constraints external to the system. The authors build dialogue environments where agents receive a reward based on the quality of the final decision they reach. The authors evaluate LMs in self-play and in collaboration with humans, finding that they fall short compared to human assistants, achieving much lower rewards despite engaging in longer dialogues. They highlight several challenges models face in decision-oriented dialogues, including goal-directed behavior, reasoning, and optimization. The authors release their environments as a testbed for future work in this area.

Stats

"We describe a class of tasks called decision-oriented dialogues, in which AI assistants such as large language models (LMs) must collaborate with one or more humans via natural language to help them make complex decisions." "We formalize three domains in which users face everyday decisions: (1) choosing an assignment of reviewers to conference papers, (2) planning a multi-step itinerary in a city, and (3) negotiating travel plans for a group of friends." "We evaluate LMs in self-play and in collaboration with humans and find that they fall short compared to human assistants, achieving much lower rewards despite engaging in longer dialogues."

Quotes

"Imagine that you are trying to book conference travel with the help of a digital assistant. Your choice of airline is flexible, but you'd rather avoid layovers, want to arrive a day or two before the conference begins, and would like to be able to check in to your hotel as soon as you arrive. Additionally, you're in charge of booking travel for a few of your colleagues, each of whom has their own preferences and budgets, some of whom will be flying in from different cities, but all of whom would like to arrive at roughly the same time and stay in a nearby area." "Difficult decision problems like these are precisely where AI assistants could shine. Automated systems can handle large amounts of information and complex computations much better than humans."

Key Insights Distilled From

Decision-Oriented Dialogue for Human-AI Collaboration

by Jessy Lin,Ni... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2305.20076.pdf

Deeper Inquiries

How could AI assistants be designed to better understand and reason about the underlying optimization problems in decision-oriented dialogues?

To enhance AI assistants' ability to understand and reason about the underlying optimization problems in decision-oriented dialogues, several strategies can be implemented: Structured Knowledge Representation: AI assistants can benefit from structured knowledge representation, where the underlying optimization problem is broken down into components that the AI can manipulate and reason about. This could involve representing the problem as a graph or a set of interconnected variables with defined relationships. Incorporating Domain Knowledge: AI assistants should be equipped with domain-specific knowledge relevant to the decision-making context. This could include information about preferences, constraints, and objectives that are crucial for making informed decisions. Utilizing Tools and External Resources: AI assistants can leverage external tools and resources to aid in decision-making. By integrating with databases, APIs, or specialized algorithms, the AI can access additional information and perform complex computations to optimize decisions. Goal-Directed Communication: AI assistants should engage in goal-directed communication with users, asking targeted questions to gather decision-relevant information and guide the dialogue towards a successful outcome. This involves understanding the user's preferences, constraints, and objectives to tailor the decision-making process effectively. Iterative Decision-Making: AI assistants can adopt an iterative approach to decision-making, where they propose solutions, receive feedback from users, and adjust their strategies based on the feedback. This iterative process allows the AI to refine its understanding of the problem and generate more optimal solutions over time.

What are the potential drawbacks or unintended consequences of AI assistants becoming too capable at making complex decisions on behalf of humans?

While highly capable AI assistants can offer numerous benefits in decision-making processes, there are potential drawbacks and unintended consequences to consider: Overreliance on AI: If humans become overly dependent on AI assistants for decision-making, they may lose critical thinking skills and decision-making abilities. This overreliance can lead to disengagement and reduced autonomy in decision-making processes. Bias and Fairness Issues: AI systems are susceptible to biases present in the data they are trained on, which can result in unfair or discriminatory decisions. Overly capable AI assistants may exacerbate these biases and perpetuate unfair outcomes in decision-making. Lack of Transparency: Highly complex AI systems may lack transparency in their decision-making processes, making it challenging for users to understand how decisions are reached. This lack of transparency can lead to distrust and skepticism towards AI recommendations. Ethical Concerns: AI assistants making complex decisions raise ethical concerns, especially in sensitive domains such as healthcare or finance. Ensuring ethical decision-making and accountability becomes crucial when AI systems have significant decision-making capabilities. Loss of Human Touch: Overly capable AI assistants may diminish the human element in decision-making processes, reducing empathy, intuition, and emotional intelligence in the decision-making context.

How might decision-oriented dialogues be extended to incorporate physical world interactions, such as a robot assistant helping a human plan a trip that involves navigating and interacting with the real environment?

Extending decision-oriented dialogues to incorporate physical world interactions involves integrating the dialogue system with real-world sensors, actuators, and interfaces. Here are some ways to achieve this integration: Sensor Integration: The robot assistant can be equipped with sensors such as cameras, GPS, and environmental sensors to perceive the physical world. These sensors provide real-time data about the environment, which can be used to inform decision-making during the trip planning process. Actuator Control: The robot assistant can have actuators like motors or manipulators to interact with the physical environment. For trip planning, actuators can be used to book tickets, make reservations, or physically navigate the environment based on the decisions made in the dialogue. Real-time Feedback Loop: The dialogue system should have a real-time feedback loop with the robot assistant, enabling seamless communication between the two. The assistant can provide instructions to the robot based on the decisions made in the dialogue, and the robot can provide feedback on the execution of those decisions. Navigation and Mapping: Incorporating navigation and mapping capabilities into the dialogue system allows the robot assistant to plan routes, avoid obstacles, and navigate the physical environment efficiently. The dialogue can include discussions on preferred routes, landmarks to visit, and transportation modes to use. Interactive Decision-Making: The dialogue can involve interactive decision-making where the human and robot collaboratively plan the trip. The robot assistant can suggest points of interest, recommend activities, and adjust plans based on user preferences expressed in the dialogue. By integrating physical world interactions into decision-oriented dialogues, the robot assistant can provide personalized and context-aware assistance to the user, enhancing the overall trip planning experience.