A Component-oriented Framework for Autonomous Agents

The design of a complex system warrants a compositional methodology, i.e., composing simple components to obtain a larger system that exhibits their collective behavior in a meaningful way. We propose an automaton-based paradigm for compositional design of such systems where an action is accompanied by one or more preferences. At run-time, these preferences provide a natural fallback mechanism for the component, while at design-time they can be used to reason about the behavior of the component in an uncertain physical world. Using structures that tell us how to compose preferences and actions, we can compose formal representations of individual components or agents to obtain a representation of the composed system. We extend Linear Temporal Logic with two unary connectives that reflect the compositional structure of the actions, and show how it can be used to diagnose undesired behavior by tracing the falsification of a specification back to one or more culpable components

Probabilistic Framework for Behavior Characterization of Traffic Participants Enabling Long Term Prediction

This research aims at developing new methods that predict the behaviors of the human driven traffic participants to enable safe operation of autonomous vehicles in complex traffic environments. Autonomous vehicles are expected to operate amongst human driven conventional vehicles in the traffic at least for the next few decades. For safe navigation they will need to infer the intents as well as the behaviors of the human traffic participants using extrinsically observable information, so that their trajectories can be predicted for a time horizon long enough to do a predictive risk analysis and gracefully avert any risky situation. This research approaches this challenge by recognizing that any maneuver performed by a human driver can be divided into four stages that depend on the surrounding context: intent determination, maneuver preparation, gap acceptance and maneuver execution. It builds on the hypothesis that for a given driver, the behavior not only spans across these four maneuver stages, but across multiple maneuvers. As a result, identifying the driver behavior in any of these stages can help characterize the nature of all the subsequent maneuvers that the driver is likely to perform, thus resulting in a more accurate prediction for a longer time horizon. To enable this, a novel probabilistic framework is proposed that couples the different maneuver stages of the observed traffic participant together and associates them to a driving style. To realize this framework two candidate Multiple Model Adaptive Estimation approaches were compared: Autonomous Multiple Model (AMM) and Interacting Multiple Model(IMM) filtering approach. The IMM approach proved superior to the AMM approach and was eventually validated using a trajectory extracted from a real world dataset for efficacy. The proposed framework was then implemented by extending the validated IMM approach with contextual information of the observed traffic participant. The classification of the driving style of the traffic participant (behavior characterization) was then demonstrated for two use case scenarios. The proposed contextual IMM (CIMM) framework also showed improvements in the performance of the behavior classification of the traffic participants compared to the IMM for the identified use case scenarios. This outcome warrants further exploration of this framework for different traffic scenarios. Further, it contributes towards the ongoing endeavors for safe deployment of autonomous vehicles on public roads

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

Formal Methods and Safety for Automated Vehicles: Modeling, Abstractions, and Synthesis of Tactical Planners

One goal of developing automated road vehicles is to completely free people from driving tasks. Automated vehicles with no human driver must handle all traffic situations that human drivers are expected to handle, possibly more. Though human drivers cause a lot of traffic accidents, they still have a very low accident and failure rate that automated vehicles must match.Tactical planners are responsible for making discrete decisions for the coming seconds or minutes. As with all subsystems in an automated vehicle, these planners need to be supported with a credible and convincing argument of their correctness. The planners interact with other road users in a feedback loop, so their correctness depends on their behavior in relation to other drivers and road users over time. One way to ascertain their correctness is to test the vehicles in real traffic. But to be sufficiently certain that a tactical planner is safe, it has to be tested on 255 million miles with no accidents.Formal methods can, in contrast to testing, mathematically prove that given requirements are fulfilled. Hence, these methods are a promising alternative for making credible arguments for tactical planners’ correctness. The topic of this thesis is the use of formal methods in the automotive industry to design safe tactical planners. What is interesting is both how automotive systems can be modeled in formal frameworks, and how formal methods can be used practically within the automotive development process.The main findings of this thesis are that it is viable to formally express desired properties of tactical planners, and to use formal methods to prove their correctness. However, the difficulty to anticipate and inspect the interaction of several desired properties is found to be an obstacle. Model Checking, Reactive Synthesis, and Supervisory Control Theory have been used in the design and development process of tactical planners, and these methods have their benefits, depending on the application. To be feasible and useful, these methods need to operate on both a high and a low level of abstraction, and this thesis contributes an automatic abstraction method that bridges this divide.It is also found that artifacts from formal methods tools may be used to convincingly argue that a realization of a tactical planner is safe, and that such an argument puts formal requirements on the vehicle’s other subsystems and its surroundings

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

Intelligent and High-Performance Behavior Design of Autonomous Systems via Learning, Optimization and Control

Nowadays, great societal demands have rapidly boosted the development of autonomous systems that densely interact with humans in many application domains, from manufacturing to transportation and from workplaces to daily lives. The shift from isolated working environments to human-dominated space requires autonomous systems to be empowered to handle not only environmental uncertainties such as external vibrations but also interaction uncertainties arising from human behavior which is in nature probabilistic, causal but not strictly rational, internally hierarchical and socially compliant.This dissertation is concerned with the design of intelligent and high-performance behavior of such autonomous systems, leveraging the strength from control, optimization, learning, and cognitive science. The work consists of two parts. In Part I, the problem of high-level hybrid human-machine behavior design is addressed. The goal is to achieve safe, efficient and human-like interaction with people. A framework based on the theory of mind, utility theories and imitation learning is proposed to efficiently represent and learn the complicated behavior of humans. Built upon that, machine behaviors at three different levels - the perceptual level, the reasoning level, and the action level - are designed via imitation learning, optimization, and online adaptation, allowing the system to interpret, reason and behave as human, particularly when a variety of uncertainties exist. Applications to autonomous driving are considered throughout Part I. Part II is concerned with the design of high-performance low-level individual machine behavior in the presence of model uncertainties and external disturbances. Advanced control laws based on adaptation, iterative learning and the internal structures of uncertainties/disturbances are developed to assure that the high-level interactive behaviors can be reliably executed. Applications on robot manipulators and high-precision motion systems are discussed in this part

Logic programming for deliberative robotic task planning

Over the last decade, the use of robots in production and daily life has increased. With increasingly complex tasks and interaction in different environments including humans, robots are required a higher level of autonomy for efficient deliberation. Task planning is a key element of deliberation. It combines elementary operations into a structured plan to satisfy a prescribed goal, given specifications on the robot and the environment. In this manuscript, we present a survey on recent advances in the application of logic programming to the problem of task planning. Logic programming offers several advantages compared to other approaches, including greater expressivity and interpretability which may aid in the development of safe and reliable robots. We analyze different planners and their suitability for specific robotic applications, based on expressivity in domain representation, computational efficiency and software implementation. In this way, we support the robotic designer in choosing the best tool for his application

Modeling Autonomous Agents In Military Simulations

Simulation is an important tool for prediction and assessment of the behavior of complex systems and situations. The importance of simulation has increased tremendously during the last few decades, mainly because the rapid pace of development in the field of electronics has turned the computer from a costly and obscure piece of equipment to a cheap ubiquitous tool which is now an integral part of our daily lives. While such technological improvements make it easier to analyze well-understood deterministic systems, increase in speed and storage capacity alone are not enough when simulating situations where human beings and their behavior are an integral part of the system being studied. The problem with simulation of intelligent entities is that intelligence is still not well understood and it seems that the field of Artificial Intelligence (AI) has a long way to go before we get computers to think like humans. Behavior-based agent modeling has been proposed in mid-80\u27s as one of the alternatives to the classical AI approach. While used mainly for the control of specialized robotic vehicles with very specific sensory capabilities and limited intelligence, we believe that a behavior-based approach to modeling generic autonomous agents in complex environments can provide promising results. To this end, we are investigating a behavior-based model for controlling groups of collaborating and competing agents in a geographic terrain. In this thesis, we are focusing on scenarios of military nature, where agents can move within the environment and adversaries can eliminate each other through use of weapons. Different aspects of agent behavior like navigation to a goal or staying in group formation, are implemented by distinct behavior modules and the final observed behavior for each agent is an emergent property of the combination of simple behaviors and their interaction with the environment. Our experiments show that while such an approach is quite efficient in terms of computational power, it has some major drawbacks. One of the problems is that reactive behavior-based navigation algorithms are not well suited for environments with complex mobility constraints where they tend to perform much worse than proper path planning. This problem represents an important research question, especially when it is considered that most of the modern military conflicts and operations occur in urban environments. One of the contributions of this thesis is a novel approach to reactive navigation where goals and terrain information are fused based on the idea of transforming a terrain with obstacles into a virtual obstacle-free terrain. Experimental results show that our approach can successfully combine the low run-time computational complexity of reactive methods with the high success rates of classical path planning. Another interesting research problem is how to deal with the unpredictable nature of emergent behavior. It is not uncommon to have situations where an outcome diverges significantly from the intended behavior of the agents due to highly complex nonlinear interactions with other agents or the environment itself. Chances of devising a formal way to predict and avoid such abnormalities are slim at best, mostly because such complex systems tend to be be chaotic in nature. Instead, we focus on detection of deviations through tracking group behavior which is a key component of the total situation awareness capability required by modern technology-oriented and network-centric warfare. We have designed a simple and efficient clustering algorithm for tracking of groups of agent suitable for both spatial and behavioral domain. We also show how to detect certain events of interest based on a temporal analysis of the evolution of discovered clusters