Advancements in Autonomous System Decision-Making

At Carl von Ossietzky University in Oldenburg, we are working on advanced systems that can manage tasks on their own. These systems can build a version of the world around them and make plans based on what they perceive. They can figure out which information is important as they complete their tasks.

#The Challenge of Information Overload

These systems often receive too much information from many sources. Not all of this information is crucial to their tasks. Our main goal is to find a reliable way for these systems to identify what information is necessary to keep them safe as they work.

For example, when a self-driving car comes to a crosswalk, it must slow down if people are trying to cross the street. The car does not need to know the color of the pedestrians' shirts or how many are waiting. However, knowing the number of people can help the car estimate when they will be out of the way, which can influence its decision about taking a detour.

#Decision Making in Autonomous Systems

The way these systems control their actions can be seen as a strategy. They select actions based on the information they have gathered. The decision they make combines what they see in the world with what they understand about it. For instance, a car may observe a speed limit and know how speed affects its movement.

Since these systems usually have limited ways to sense their surroundings and communicate, they often face uncertainty. At any given time, several different scenarios could be true, and they cannot always know which one is closest to reality.

#Goals of Research

Our research looks at what these systems need to perceive and know to make good decisions. We want to understand what Observations and Knowledge are vital for their success in critical situations. We argue that the system does not need complete information to perform well.

To put our ideas into practice, we are developing a model that clearly shows how these systems form Beliefs. Using this model, we can define what it means for information to be relevant to the system's Decision-making process.

#Relevance and Decision Making

In our view, something is relevant if it is necessary for the system to achieve its goals. A combination of knowledge, observations, and beliefs is deemed relevant if omitting any of these would lead to less effective performance.

We present a method to determine relevant combinations of knowledge, observations, and beliefs. This approach will be particularly useful in the early stages of designing these systems, where simple models are often used.

#The Design-Time World Model

We assume that engineers will create a model of the world that defines the task the system will perform. This model may come from databases of different scenarios and criteria for testing.

Before we analyze the necessary information for the system, we also assume that the potential beliefs the system can hold have already been defined. This includes which objects and relationships will be represented in its beliefs.

#Addressing Uncertainty

The system often operates under uncertainty. Even when the situation is unclear, it needs to act. For example, if a self-driving car encounters a slippery road, it must adjust its actions based on its estimate of the conditions.

The system's main goals include saving time and avoiding collisions. Decisions about whether to turn or stop must be made quickly, often without complete information about the conditions ahead.

#Model Structure

We are developing a structured approach to help understand how these systems form beliefs. Our model explicitly represents the beliefs of the system and examines how they play into decision-making. A system is deemed rational if it picks actions it believes will succeed.

#Relevance and Information Processing

The central concept of our work is determining what information is relevant for a safety-critical autonomous system. We explore how the definitions of relevance used in other fields, like information retrieval, can apply to our context.

#Observing Related Work

Relevance has been discussed in various domains, including philosophy, psychology, and information science. In information retrieval, relevance has been a key challenge since the early days when librarians sought to find the right documents for users.

The concept of relevance involves a relationship between information and a user's needs. This relationship can be broken down into different categories, such as system relevance, topical relevance, and cognitive relevance. Understanding how these dimensions interact is crucial for our research.

#The Nature of Relevance

Relevance is a dynamic and subjective concept impacted by multiple factors. In our work, we adapt these ideas to determine what observations and knowledge are necessary for autonomous safety-critical systems.

#Autonomous Safety-Critical Systems

We are focused on the relevance of perceptions and knowledge related to autonomous safety-critical systems. While the retrieval of relevant documents may seem unrelated to the necessary input for an autonomous system, both tackle the idea of identifying what information is crucial for successful outcomes.

We emphasize that the information and knowledge needed can significantly differ from conventional information retrieval situations. We want to aid in designing systems where engineers can define the needed observations and knowledge to ensure successful operations.

#Observations and Knowledge

The relationship between observations, knowledge, and beliefs is fundamental. An autonomous system must act based on its assessment of the situation, even if that situation is uncertain. We are looking at the nature of that information and how it is processed during critical decision-making.

As systems often face evolving situations, the need for robust processing capabilities becomes crucial as they must adapt. We are interested in how these systems evaluate their knowledge and the consequences of their beliefs in varying contexts.

#Design Process for Autonomous Systems

Throughout our research, we aim to guide the design of autonomous systems with clear relationships between observations, knowledge, and beliefs. We believe that by doing this, we can create more effective systems that respond intelligently to their surroundings.

We expect that this work will facilitate the development of systems that can handle complexities in uncertain environments while achieving their objectives.

#Conclusion

Our work is centered on understanding and applying the notion of relevance in the context of safety-critical autonomous systems. By examining how these systems process information, we can help ensure that they operate effectively and safely in the real world.

We believe that as these systems evolve, they will play an increasingly significant role in society, providing a foundation for future innovations in autonomous technology. Through our research, we aim to contribute valuable insights into the design and implementation of systems that meet the demands of their environments.

#Future Work

Looking forward, we plan to further explore how the concepts we have developed can be implemented in practical applications. There will be an emphasis on refining our models and enhancing their adaptability to various contexts. We anticipate our findings will pave the way for better-designed autonomous systems equipped for real-world challenges, ensuring safety and efficiency in their operations.

By laying the groundwork in understanding the relevance of information processing in autonomous systems, we hope to drive progress in this exciting field. This could ultimately lead to advancements that transform how we interact with technology in our daily lives.