Behavior Trees in Robotics and AI: An Introduction

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

Investigation of a teleo-reactive approach for the development of autonomic manager systems

As the demand for more capable and more feature-rich software increases, the complexity in design, implementation and maintenance also increases exponentially. This becomes a problem when the complexity prevents developers from writing, improving, fixing or otherwise maintaining software to meet specified demands whilst still reaching an acceptable level of robustness. When complexity becomes too great, the software becomes impossible to effectively be managed by even large teams of people. One way to address the problem is an Autonomic approach to software development. Autonomic software aims to tackle complexity by allowing the software to manage itself, thus reducing the need for human intervention and allowing it to reach a maintainable state. Many techniques have been investigated for development of autonomic systems including policy-based designs, utility-functions and advanced architectures. A unique approach to the problem is the teleo-reactive programming paradigm. This paradigm offers a robust and simple structure on which to develop systems. It allows the developer the freedom to express their intentions in a logical manner whilst the increased robustness reduces the maintenance cost. Teleo-Reactive programming is an established solution to low-level agent based problems such as robot navigation and obstacle avoidance, but this technique shows behaviour which is consistent with higher-level autonomic solutions. This project therefore investigates the extent of the applicability of teleo-reactive programming as an autonomic solution. Can the technique be adapted to allow a more ideal fitness for purpose' for autonomics whilst causing minimal changes to the tried and tested original structure and meaning? Does the technique introduce any additional problems and can these be addressed with improvements to the teleo-reactive framework? Teleo-Reactive programming is an interesting approach to autonomic computing because in a Teleo-Reactive program, its state is not predetermined at any moment in time and is based on a priority system where rules execute based on the current environmental context (i.e. not in any strict procedural way) whilst still aiming at the intended goal. This method has been shown to be very robust and exhibits some of the qualities of autonomic software

Designing Behavior Trees from Goal-Oriented LTLf Formulas

Temporal logic can be used to formally specify autonomous agent goals, but synthesizing planners that guarantee goal satisfaction can be computationally prohibitive. This paper shows how to turn goals specified using a subset of finite trace Linear Temporal Logic (LTL) into a behavior tree (BT) that guarantees that successful traces satisfy the LTL goal. Useful LTL formulas for achievement goals can be derived using achievement-oriented task mission grammars, leading to missions made up of tasks combined using LTL operators. Constructing BTs from LTL formulas leads to a relaxed behavior synthesis problem in which a wide range of planners can implement the action nodes in the BT. Importantly, any successful trace induced by the planners satisfies the corresponding LTL formula. The usefulness of the approach is demonstrated in two ways: a) exploring the alignment between two planners and LTL goals, and b) solving a sequential key-door problem for a Fetch robot.Comment: Accepted as "Most Visionary Paper" in Autonomous Robots and Multirobot Systems (ARMS) 2023 workshop affiliated with the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023

Should it stay or should it go? : A critical reflection on the critical period for language

This paper tries to shed light on traditional and current observations that give support to the idea that language is subject to critical period effects. It is suggested that this idea is not adequately grounded on a view on language as a developmental phenomenon which motivates the suggestion of moving from the now classic concept of language as a 'faculty' to a new concept of language as a 'gradient': i.e. an aggregate of cognitive abilities, the weight of which is variable from one to another developmental stage, and which exercise crucial scaffolding effects on each other. Once this well-supported view is assumed, the idea of 'critical period' becomes an avoidable one, for language can instantiate different forms of gradation, none of which is inherently normal or deviant relatively to each other. In any event, a notion of 'criticality' is retained within this view, yet simply to name the transitional effects of scaffolding influences within the gradien

Artificial Intelligence Research Branch future plans

This report contains information on the activities of the Artificial Intelligence Research Branch (FIA) at NASA Ames Research Center (ARC) in 1992, as well as planned work in 1993. These activities span a range from basic scientific research through engineering development to fielded NASA applications, particularly those applications that are enabled by basic research carried out in FIA. Work is conducted in-house and through collaborative partners in academia and industry. All of our work has research themes with a dual commitment to technical excellence and applicability to NASA short, medium, and long-term problems. FIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at the Jet Propulsion Laboratory (JPL) and AI applications groups throughout all NASA centers. This report is organized along three major research themes: (1) Planning and Scheduling: deciding on a sequence of actions to achieve a set of complex goals and determining when to execute those actions and how to allocate resources to carry them out; (2) Machine Learning: techniques for forming theories about natural and man-made phenomena; and for improving the problem-solving performance of computational systems over time; and (3) Research on the acquisition, representation, and utilization of knowledge in support of diagnosis design of engineered systems and analysis of actual systems