Rationality: Building AI Agents That Makes Sense

We've all seen AI do amazing things, from composing music to diagnosing diseases. But have you ever wondered why an AI made a particular decision? Or whether that decision was truly the best one it could have made at the time?

As AI systems become increasingly powerful, understanding the concept of rationality - the ability to reason and make optimal choices becomes paramount. In my previous post, we explored the fascinating world of AI agents and their capabilities. Now, we'll go deeper into the cornerstone of intelligent behavior: rationality.

"This isn't just about making AI smarter; it's about making it better - more reliable, responsible, and aligned with our goals."

Join me as we explore the fascinating world of rational agents and how we can build AI that not only thinks but also reasons effectively.

Rationality: The Formal Definition of Intelligence

My favorite book, "Artificial Intelligence: A Modern Approach," defines rationality as the formal definition of intelligence. It's not just about having knowledge; it's about using that knowledge effectively to achieve desired outcomes. We can further break down intelligence into two key components: the thought process - the ability to think and understand & reasoning - the ability to interpret information, make inferences, and predict the best course of action.

Rationality is what drives an agent towards success, providing the foundation for its reliability and responsibility. In essence, a rational agent is one that consistently makes the best possible decisions given its knowledge and the situation at hand.   

Rationality in Action [Example: Salon Appointment Booking]

A rational agent would use all the information available to make the best possible booking decisions. As an example of an salon appointment booking agent, It would consider factors like:

• Customer preferences: If you prefer a specific stylist, the agent would try to book you with that stylist whenever possible.

• Stylist availability: The agent would ensure that appointments are only scheduled when the chosen stylist is actually available.

• Time constraints:  If you need an appointment urgently, the agent would prioritize finding you the earliest available slot.

• Efficiency: The agent would try to minimize gaps in the schedule and avoid overbooking any stylist.

Measuring AI Agent's Rationality: Inputs and Outputs

So, how do we determine if an agent is rational? Let's imagine the same AI agent designed to book appointments for a busy hair salon. Here's how we can break down its rationality and how we measure its performance:

• Environment: The environment includes things like the salon's schedule, the stylists' availability, customer preferences (like preferred stylists or services), and even real-time events (like a stylist running late or a sudden cancellation).

• Percepts: The agent receives information like appointment requests from customers ("I want a haircut with Sarah on Tuesday afternoon"), updates on stylist availability ("John is out sick today"), and changes in the schedule ("The 3 PM appointment with Lisa was canceled").

• Actions: Based on this information, the agent takes actions like scheduling appointments ("Okay, I've booked you with Sarah for Tuesday at 2 PM"), suggesting alternative times ("Sarah is fully booked on Tuesday, but she has openings on Wednesday"), or notifying customers about changes ("Unfortunately, your appointment with John needs to be rescheduled due to his absence").

• Sequence of States: These actions lead to changes in the salon's schedule. For example, a previously empty slot on Tuesday afternoon is now filled with your appointment, or a slot that was booked with John is now open.

• Desirable Outcome/Goal: The agent's goal is to maximize booked appointments while ensuring customer satisfaction and efficient use of the stylists' time. This means avoiding double-bookings, minimizing wait times, and accommodating customer preferences as much as possible.

• Performance Measure: We can measure the agent's performance by looking at metrics like the number of successful bookings, customer satisfaction ratings, stylist utilization, and the number of scheduling conflicts or cancellations.

To figure out if an AI agent is rational, we look at what it's trying to do (its goal), how it interacts with its surroundings (its actions in the environment), and how well it achieves its goal (its performance). Just like with our salon booking agent, different AI agents have different goals and environments, and their performance is measured accordingly.

What Influences Rationality? The Kid and the Math Book Analogy

To understand what makes an AI agent rational, think of a child learning about triangles and circles from a math book. If you ask them a geometry question, how well they do depends on a few things:

• Knowledge: Did the book explain the concepts clearly? (Does the AI have good information?)

• Reasoning: Did the child understand the concepts and can they apply them to new problems? (Can the AI use its knowledge to reason and solve problems?)

• Context: Is the question related to what the child learned in the book? (Does the AI understand the situation it's in?)

• Goals: Was the child actually trying to learn geometry, or were they supposed to be studying something else? (Does the AI have clear goals and is it working towards them?)

Just like with the child, an AI agent needs the right knowledge, reasoning abilities, understanding of context, and aligned goals to be truly rational. If any of these are missing, its actions might not make sense, and it might not achieve the desired outcomes.

The Elusive Ideal: Perfect Rationality

In an ideal world, we might strive for perfect rationality. This would mean creating an agent that always makes the absolute best decision, maximizing its chances of success in any given situation. Let's illustrate this with a simple Question Answering (QnA) example:

Imagine we're building a QnA chatbot agent designed to answer questions about world capitals. A perfectly rational agent, in this context, would always provide the correct capital for any country queried.

Let's break down how this relates to the concept of perfect rationality:

• Environment (E): The environment for our QnA agent consists of all possible questions about world capitals it might receive. For example, the environment could include questions like, "What is the capital of France?", "What is the capital of Japan?", "What is the capital of Brazil?", and so on.

• Performance Measure (U): The performance measure is straightforward:

  • The agent receives a score of +1 for each correct answer.

  • The agent receives a score of 0 for each incorrect answer.

  • The total performance is the sum of scores across all questions asked within a given time frame.

• Agent Function (f): This is the internal logic of the QnA agent. It's the set of rules and algorithms that determine how the agent processes a question and generates an answer. For example, one agent function might involve a simple lookup in a database of countries and capitals. Another, more sophisticated function might use a large language model trained on a vast amount of text data.

• Expected Value (V): For each possible agent function (f), we can calculate its expected value (V) in the environment (E) based on the performance measure (U). This is essentially the average score we expect the agent to achieve over many questions.

Now, a perfectly rational agent would have the agent function that yields the highest possible expected value (V). In our QnA example, this means the agent would have a function that always returns the correct capital city for any country in the environment. It might achieve this through a flawless, comprehensive internal database or a perfectly trained language model.

In simpler terms: Our perfectly rational QnA agent would be like an all-knowing oracle for capital cities, never making a mistake.

Why is this "elusive?" In reality, creating such a perfectly rational QnA agent is incredibly difficult. The agent might encounter:

• Ambiguous Questions: "What's the capital of the States?" (Could refer to the United States or another country with "States" in its name).

• Unforeseen Questions: Questions about newly formed countries or capitals that have recently changed.

• Data Limitations: The agent's internal database or training data might be incomplete or outdated.

  
  

Moreover, in complex scenarios - it might require seriously high computing power for an agent to successfully complete a task. Therefore, while perfect rationality serves as a useful theoretical benchmark, it's rarely achievable in practice. The agent might not have access to all the information, or the world itself might change, rendering its knowledge outdated.

The Practical Reality: Limited/Well-Balanced/Calculated Rationality

However, as we've established, achieving this ideal of perfect rationality is often impossible. This leads us to a more practical and realistic perspective: that of limited, or bounded, rationality.

While perfect rationality is a compelling theoretical concept, it's often unattainable in the real world. Agents, like humans, operate under constraints (as described earlier). This is where the concept of limited rationality comes into play.

Formally speaking, limited rationality means acting appropriately when there is not enough time or information available to perform all the computations one might like. It's about making the best possible decision given the available resources.

Another related concept is calculative rationality (essentially, rationality with unlimited computational power). A calculative rational agent is one that, if given infinite time to compute, would arrive at the perfectly rational decision. However, as Herbert Simon pointed out, perfectly rational agents do not exist. A calculative rational chess program might identify the optimal move, but it might take an impractical amount of time to do so. In such cases, calculative rationality is not necessarily desirable.

Therefore, It's crucial to understand that being rational is different from being perfect. A rational agent aims to make the best decision based on the information it has at the time, which maximizes its chances of a good outcome. Perfection, on the other hand, would require knowing the actual best action in hindsight, which is impossible. Expecting an agent to be perfect is not only unrealistic, it sets an impossible design standard.

Its a tradeoff!

Improving Rationality Under Constraints:

As we discussed, real-world agent space is demanding and agents might face computational limits, yet several strategies can enhance their rationality. The paper "Artificial Intelligence, Rationality, and Intelligence" by Kelly Cho highlights three key approaches:

1. Inductive Learning: Agents can improve their knowledge base by learning patterns and rules from data. For example, suppose an LLM-based customer service chatbot is trained to handle product-related queries. By analyzing a large dataset of historical interactions, the agent can learn common rules, such as:

   • If the query mentions "warranty," respond with warranty policies.

   • If the query involves "return," prioritize linking to the return process.

2. Reinforcement Learning (RL): RL enables agents to learn by interacting with environments. Techniques like Q-learning and Deep Q Networks (DQNs) help agents optimize policies, while hierarchical RL allows for task decomposition. For instance, imagine an LLM-based conversational agent that suggests movies based on user preferences. Using RL:

   • The agent experiments with suggestions (e.g., "How about Inception?").

   • It receives feedback (positive if the user likes the suggestion, neutral/negative if not).

   • Over time, it learns to prioritize genres, directors, or themes preferred by the user.

While these approaches show promise, integrating multiple mechanisms is complex. Techniques like meta-learning and transfer learning are also being explored to address these challenges and improve rationality.

Types of Rationalization: Making Sense of Decisions:

As AI agents become more integrated into our lives, it's not enough for them to simply make decisions; they also need to be able to explain their reasoning, especially in multi-agent settings like negotiations. This ability to rationalize their choices builds trust and transparency, which are crucial especially for use cases calling for human-AI collaboration.

As described in the paper "Agents that Rationalize their Decisions," we can identify three main types of rationalization:

• Goal-based Rationalization: The agent justifies its decision by showing how it aligns with the goals of the audience. For example, "I chose this option because it helps you achieve your objective of maximizing profits."

• Belief-based Rationalization: The agent explains its decision based on the beliefs of the audience. For instance, "I selected this candidate because, as you know, they have the most relevant experience for the job."

• Assumption-based Rationalization: The agent uses abductive reasoning – essentially, making an educated guess – to create a justification for its decision. This might involve selecting from its own beliefs or even inventing a plausible explanation. For example, "I believe this marketing strategy will be effective because it targets a new demographic."

To illustrate how these types might play out in a real-world scenario, consider a hiring decision. A goal-based rationalization might be, "I chose Candidate A because they will help us increase sales." A belief-based rationalization could be, "I chose Candidate A because, as we discussed, cultural fit is important, and they seem like a great match." An assumption-based rationalization might be, "I have a hunch that Candidate A is a fast learner and will quickly adapt to the role, even though their resume doesn't explicitly show it."

It's important to note that an agent might rationalize its decisions based solely on the perceived goals and beliefs of the audience they are being designed for, even if those rationalizations conflict with the agent's own internal goals and beliefs. We will discuss this in upcoming blogs.

[New] Information Gathering as a Rationalisation Strategy:

AI agents can enhance performance by gathering more information when faced with ambiguity. For instance, a QnA agent might clarify, "Are you asking about the capital of the United States or another country with 'States' in its name?" Such follow-ups reduce drift and improve precision.

This is particularly relevant for Large Language Models (LLMs) like Gemini and GPT models, which can struggle with hallucination, providing plausible but incorrect answers. The paper "Sufficient Context: A New Lens On Retrieval Augmented Generation Systems" explores improving LLMs by enhancing their ability to ask clarifying questions or abstain when context is insufficient.

Engaging with Current Events: Rationality in Practice

Rationality plays a critical role in ongoing AI advancements:

1. LLMs and Hallucination: Systems like ChatGPT demonstrate the need for rationality in ensuring accurate and contextually relevant responses. For example, when faced with ambiguous queries, rational agents could proactively gather additional context to avoid errors.

2. Autonomous Vehicles: Companies like Waymo and Tesla rely on rational decision-making to navigate real-world scenarios safely. Rationality is critical in deciding between avoiding collisions and minimizing damage.

3. Healthcare AI: Systems like DeepMind’s AlphaFold, which predicts protein structures, showcase the power of rationality in solving complex problems. Ensuring these systems act rationally under uncertainty can improve patient outcomes and build trust.

4. AI Ethics and Alignment: The broader alignment problem highlights the importance of rationality in ensuring AI systems act in line with human values. Rational agents must balance maximizing performance with ethical considerations in high-stakes domains like governance and finance.

Conclusion:

We've journeyed from the basic definition of rationality to the complexities of building rational agents in a resource-constrained world. Rationality is not just about intelligence; it's about making the best possible decisions given available knowledge, context, and computational power. As AI agents become increasingly integrated into our lives, understanding and improving their rationality is paramount. It is the key to creating AI systems that are not only powerful but also reliable, responsible, and aligned with human values. The pursuit of rationality in AI is not merely a technical challenge; it's a crucial step towards building a future where humans and AI can collaborate effectively and ethically.

So, how do we ensure that the next generation of AI agents are not only smart but also demonstrably rational? What steps can we take today to build a future where humans and AI collaborate effectively, guided by shared principles of reason and understanding? I'd love to hear your thoughts in the comments below! What kind of future do you envision with Rational AI Agents?