A Behavior Tree (BT) is a way to structure the switching between different tasks in an autonomous agent, such as a robot or a virtual entity in a computer game. BTs are a very efficient way of creating complex systems that are both modular and reactive. These properties are crucial in many applications, which has led to the spread of BT from computer game programming to many branches of AI and Robotics. In this book, we will first give an introduction to BTs, then we describe how BTs relate to, and in many cases generalize, earlier switching structures. These ideas are then used as a foundation for a set of efficient and easy to use design principles. Properties such as safety, robustness, and efficiency are important for an autonomous system, and we describe a set of tools for formally analyzing these using a state space description of BTs. With the new analysis tools, we can formalize the descriptions of how BTs generalize earlier approaches. We also show the use of BTs in automated planning and machine learning. Finally, we describe an extended set of tools to capture the behavior of Stochastic BTs, where the outcomes of actions are described by probabilities. These tools enable the computation of both success probabilities and time to completion.

This week I attended an "Artificial Intelligence (AI) Roundtable" of leading scientists, entrepreneurs and venture investors. As the discussion focused mainly on basic statistical techniques, I left feeling unfulfilled. My friend, Matt Turck, recently wrote that "just about every major tech company is working very actively on AI," which also means that every startup hungry for capital is purchasing a dot'ai' domain name. As the lines blur between what is and what really isn't, I feel it necessary to provide readers with a quick lens of how to view intelligent agents for mechatronics. For 65 years, The Turing Test remained unsolvable until a computer program called "Eugene Goostman" conquered it in 2014.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Behavior-based and reactive control methods are popular choices for building fast and lightweight intelligent controllers for resource-constrained systems. Reactive methods are extremely useful in highly resource-constrained applications, but at a cost: they tend to be even more susceptible to certain types of failures than deliberative techniques. Without a planner to adapt to changes, even a small failure can result in incorrect behavior from the entire controller. In this paper, we propose extensions to behavior-based subsumption that can detect four types of failures.

The dendritic cell algorithm is an immune-inspired technique for processing time-dependant data. Here we propose it as a possible solution for a robotic classification problem. The dendritic cell algorithm is implemented on a real robot and an investigation is performed into the effects of varying the migration threshold median for the cell population. The algorithm performs well on a classification task with very little tuning. Ways of extending the implementation to allow it to be used as a classifier within the field of robotic security are suggested.

We argue that the time between mission conception and implementation can be radically reduced, that launch mass can be slashed, that totally autonomous robots can be more reliable than ground controlled robots, and that large numbers of robots can change the tradeoff between reliability of individual components and overall mission success. Lastly, we suggest that within a few years it will be possible at modest cost to invade a planet with millions of tiny robotsJournal of The British Interplanetary Society, Vol. 42, pp 478-485