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

An FSM Re-Engineering Approach to Sequential Circuit Synthesis by State Splitting

We propose Finite State Machine (FSM) re-engineering, a performance enhancement framework for FSM synthesis and optimization. It starts with the traditional FSM synthesis procedure, then proceeds to re-construct a functionally equivalent but topologically different FSM based on the optimization objective, and concludes with another round of FSM synthesis on the re-constructed FSM. This approach explores a larger solution space that consists of a set of FSMs functionally equivalent to the original one, making it possible to obtain better solutions than in the original FSM. Guided by the result from the #2;rst round of synthesis, the solution space exploration process can be rapid and cost-ef#2;cient. We apply this framework to FSM state encoding for power minimization and area minimization. The FSM is #2;rst minimized and encoded using existing state encoding algorithms. Then we develop both a heuristic algorithm and a genetic algorithm to re-construct the FSM. Finally, the FSM is reencoded by the same encoding algorithms. To demonstrate the effectiveness of this framework, we conduct experiments on MCNC91 sequential circuit benchmarks. The circuits are read in and synthesized in SIS environment. After FSM re-engineering are performed, we measure the power, area and delay in the newly synthesized circuits. In the powerdriven synthesis, we observe an average 5.5% of total power reduction with 1.3% area increase and 1.3% delay increase. This results are in general better than other low power state encoding techniques on comparable cases. In the area-driven synthesis, we observe an average 2.7% area reduction, 1.8% delay reduction, and 0.4% power increase. Finally, we use integer linear programming to obtain the optimal low power state encoding for benchmarks of small size. We #2;nd that the optimal solutions in the re- engineered FSMs are 1% to 8% better than the optimal solutions in the original FSMs in terms of power minimization

Quantifying Resource Use in Computations

It is currently not possible to quantify the resources needed to perform a computation. As a consequence, it is not possible to reliably evaluate the hardware resources needed for the application of algorithms or the running of programs. This is apparent in both computer science, for instance, in cryptanalysis, and in neuroscience, for instance, comparative neuro-anatomy. A System versus Environment game formalism is proposed based on Computability Logic that allows to define a computational work function that describes the theoretical and physical resources needed to perform any purely algorithmic computation. Within this formalism, the cost of a computation is defined as the sum of information storage over the steps of the computation. The size of the computational device, eg, the action table of a Universal Turing Machine, the number of transistors in silicon, or the number and complexity of synapses in a neural net, is explicitly included in the computational cost. The proposed cost function leads in a natural way to known computational trade-offs and can be used to estimate the computational capacity of real silicon hardware and neural nets. The theory is applied to a historical case of 56 bit DES key recovery, as an example of application to cryptanalysis. Furthermore, the relative computational capacities of human brain neurons and the C. elegans nervous system are estimated as an example of application to neural nets.Comment: 26 pages, no figure

Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation

New communication standards need to deal with machine-to-machine communications, in which users may start or stop transmitting at any time in an asynchronous manner. Thus, the number of users is an unknown and time-varying parameter that needs to be accurately estimated in order to properly recover the symbols transmitted by all users in the system. In this paper, we address the problem of joint channel parameter and data estimation in a multiuser communication channel in which the number of transmitters is not known. For that purpose, we develop the infinite factorial finite state machine model, a Bayesian nonparametric model based on the Markov Indian buffet that allows for an unbounded number of transmitters with arbitrary channel length. We propose an inference algorithm that makes use of slice sampling and particle Gibbs with ancestor sampling. Our approach is fully blind as it does not require a prior channel estimation step, prior knowledge of the number of transmitters, or any signaling information. Our experimental results, loosely based on the LTE random access channel, show that the proposed approach can effectively recover the data-generating process for a wide range of scenarios, with varying number of transmitters, number of receivers, constellation order, channel length, and signal-to-noise ratio.Comment: 15 pages, 15 figure

Measurements, Models, Systems and Design

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A control algorithm for autonomous optimization of extracellular recordings

This paper develops a control algorithm that can autonomously position an electrode so as to find and then maintain an optimal extracellular recording position. The algorithm was developed and tested in a two-neuron computational model representative of the cells found in cerebral cortex. The algorithm is based on a stochastic optimization of a suitably defined signal quality metric and is shown capable of finding the optimal recording position along representative sampling directions, as well as maintaining the optimal signal quality in the face of modeled tissue movements. The application of the algorithm to acute neurophysiological recording experiments and its potential implications to chronic recording electrode arrays are discussed