Uma comparação entre arquiteturas cognitivas : análise teórica e prática

Orientador: Ricardo Ribeiro GudwinDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Este trabalho apresenta uma comparação teórica e prática entre três das mais populares arquiteturas cognitivas: SOAR, CLARION e LIDA. A comparação teórica é realizada com base em um conjunto de funções cognitivas supostamente existentes no ciclo cognitivo humano. A comparação prática é realizada aplicando-se um mesmo experimento em todas as arquiteturas, coletando alguns dados e comparando-as usando como base algumas métricas de qualidade de software. O objetivo é enfatizar semelhanças e diferenças entre os modelos e implementações, com o objetivo de aconselhar um novo usuário a escolher a arquitetura mais apropriada para uma certa aplicaçãoAbstract: This work presents a theoretical and practical comparison of three popular cognitive architectures: SOAR, CLARION, and LIDA. The theoretical comparison is performed based on a set of cognitive functions supposed to exist in the human cognitive cycle. The practical comparison is performed applying the same experiment in all architectures, collecting some data and comparing them using a set of software quality metrics as a basis. The aim is to emphasize similarities and differences among the models and implementations, with the purpose to advise a newcomer on how to choose the appropriated architecture for an applicationMestradoEngenharia de ComputaçãoMestre em Engenharia Elétric

COMBINED ARTIFICIAL INTELLIGENCE BEHAVIOUR SYSTEMS IN SERIOUS GAMING

This thesis proposes a novel methodology for creating Artificial Agents with semi-realistic behaviour, with such behaviour defined as overcoming common limitations of mainstream behaviour systems; rapidly switching between actions, ignoring “obvious” event priorities, etc. Behaviour in these Agents is not fully realistic as some limitations remain; Agents have a “perfect” knowledge about the surrounding environment, and an inability to transfer knowledge to other Agents (no communication). The novel methodology is achieved by hybridising existing Artificial Intelligence (AI) behaviour systems. In most artificial agents (Agents) behaviour is created using a single behaviour system, whereas this work combines several systems in a novel way to overcome the limitations of each. A further proposal is the separation of behavioural concerns into behaviour systems that are best suited to their needs, as well as describing a biologically inspired memory system that further aids in the production of semi-realistic behaviour. Current behaviour systems are often inherently limited, and in this work it is shown that by combining systems that are complementary to each other, these limitations can be overcome without the need for a workaround. This work examines in detail Belief Desire Intention systems, as well as Finite State Machines and explores how these methodologies can complement each other when combined appropriately. By combining these systems together a hybrid system is proposed that is both fast to react and simple to maintain by separating behaviours into fast-reaction (instinctual) and slow-reaction (behavioural) behaviours, and assigning these to the most appropriate system. Computational intelligence learning techniques such as Artificial Neural Networks have been intentionally avoided, as these techniques commonly present their data in a “black box” system, whereas this work aims to make knowledge explicitly available to the user. A biologically inspired memory system has further been proposed in order to generate additional behaviours in Artificial Agents, such as behaviour related to forgetfulness. This work explores how humans can quickly recall information while still being able to store millions of pieces of information, and how this can be achieved in an artificial system

Executive Attention, Action Selection and Attention-Based Learning in Neurally Controlled Autonomous Agents

I describe the design and implementation of an integrated neural architecture, modelled on human executive attention, which is used to control both automatic (reactive) and willed action selection in a simulated robot. The model, based upon Norman and Shallice's supervisory attention system, incorporates important features of human attentional control: selection of an intended task over a more salient automatic task; priming of future tasks that are anticipated; and appropriate levels of persistence of focus of attention. Recognising that attention-based learning, mediated by the limbic system, and the hippocampus in particular, plays an important role in adaptive learning, I extend the Norman and Shallice model, introducing an intrinsic, attention-based learning mechanism that enhances the automaticity of willed actions and reduces future need for attentional effort required for dealing with distractions. These enhanced features support a new level of attentional autonomy in the operation of the simulated robot. Some properties of the model are explored using lesion studies, leading to the identification of a correspondence between the behavioural pathologies of the simulated robot and those seen in human patients suffering dysfunction of executive attention

컴퓨터를 활용한 여러 사람의 동작 연출

학위논문 (박사)-- 서울대학교 대학원 공과대학 전기·컴퓨터공학부, 2017. 8. 이제희.Choreographing motion is the process of converting written stories or messages into the real movement of actors. In performances or movie, directors spend a consid-erable time and effort because it is the primary factor that audiences concentrate. If multiple actors exist in the scene, choreography becomes more challenging. The fundamental difficulty is that the coordination between actors should precisely be ad-justed. Spatio-temporal coordination is the first requirement that must be satisfied, and causality/mood are also another important coordinations. Directors use several assistant tools such as storyboards or roughly crafted 3D animations, which can visu-alize the flow of movements, to organize ideas or to explain them to actors. However, it is difficult to use the tools because artistry and considerable training effort are required. It also doesnt have ability to give any suggestions or feedbacks. Finally, the amount of manual labor increases exponentially as the number of actor increases. In this thesis, we propose computational approaches on choreographing multiple actor motion. The ultimate goal is to enable novice users easily to generate motions of multiple actors without substantial effort. We first show an approach to generate motions for shadow theatre, where actors should carefully collaborate to achieve the same goal. The results are comparable to ones that are made by professional ac-tors. In the next, we present an interactive animation system for pre-visualization, where users exploits an intuitive graphical interface for scene description. Given a de-scription, the system can generate motions for the characters in the scene that match the description. Finally, we propose two controller designs (combining regression with trajectory optimization, evolutionary deep reinforcement learning) for physically sim-ulated actors, which guarantee physical validity of the resultant motions.Chapter 1 Introduction 1 Chapter 2 Background 8 2.1 Motion Generation Technique 9 2.1.1 Motion Editing and Synthesis for Single-Character 9 2.1.2 Motion Editing and Synthesis for Multi-Character 9 2.1.3 Motion Planning 10 2.1.4 Motion Control by Reinforcement Learning 11 2.1.5 Pose/Motion Estimation from Incomplete Information 11 2.1.6 Diversity on Resultant Motions 12 2.2 Authoring System 12 2.2.1 System using High-level Input 12 2.2.2 User-interactive System 13 2.3 Shadow Theatre 14 2.3.1 Shadow Generation 14 2.3.2 Shadow for Artistic Purpose 14 2.3.3 Viewing Shadow Theatre as Collages/Mosaics of People 15 2.4 Physics-based Controller Design 15 2.4.1 Controllers for Various Characters 15 2.4.2 Trajectory Optimization 15 2.4.3 Sampling-based Optimization 16 2.4.4 Model-Based Controller Design 16 2.4.5 Direct Policy Learning 17 2.4.6 Deep Reinforcement Learning for Control 17 Chapter 3 Motion Generation for Shadow Theatre 19 3.1 Overview 19 3.2 Shadow Theatre Problem 21 3.2.1 Problem Definition 21 3.2.2 Approaches of Professional Actors 22 3.3 Discovery of Principal Poses 24 3.3.1 Optimization Formulation 24 3.3.2 Optimization Algorithm 27 3.4 Animating Principal Poses 29 3.4.1 Initial Configuration 29 3.4.2 Optimization for Motion Generation 30 3.5 Experimental Results 32 3.5.1 Implementation Details 33 3.5.2 Animation 34 3.5.3 3D Fabrication 34 3.6 Discussion 37 Chapter 4 Interactive Animation System for Pre-visualization 40 4.1 Overview 40 4.2 Graphical Scene Description 42 4.3 Candidate Scene Generation 45 4.3.1 Connecting Paths 47 4.3.2 Motion Cascade 47 4.3.3 Motion Selection For Each Cycle 49 4.3.4 Cycle Ordering 51 4.3.5 Generalized Paths and Cycles 52 4.3.6 Motion Editing 54 4.4 Scene Ranking 54 4.4.1 Ranking Criteria 54 4.4.2 Scene Ranking Measures 57 4.5 Scene Refinement 58 4.6 Experimental Results 62 4.7 Discussion 65 Chapter 5 Physics-based Design and Control 69 5.1 Overview 69 5.2 Combining Regression with Trajectory Optimization 70 5.2.1 Simulation and Motor Skills 71 5.2.2 Control Adaptation 75 5.2.3 Control Parameterization 79 5.2.4 Efficient Construction 81 5.2.5 Experimental Results 84 5.2.6 Discussion 89 5.3 Example-Guided Control by Deep Reinforcement Learning 91 5.3.1 System Overview 92 5.3.2 Initial Policy Construction 95 5.3.3 Evolutionary Deep Q-Learning 100 5.3.4 Experimental Results 107 5.3.5 Discussion 114 Chapter 6 Conclusion 119 6.1 Contribution 119 6.2 Future Work 120 요약 135Docto

Towards Automated Design of Complex Modular Systems Inspired by Nature

Katedra kybernetik

Autonomy of Military Robots: Assessing the Technical and Legal (“Jus In Bello”) Thresholds, 32 J. Marshall J. Info. Tech. & Privacy L. 57 (2016)

While robots are still absent from our homes, they have started to spread over battlefields. However, the military robots of today are mostly remotely controlled platforms, with no real autonomy. This paper will disclose the obstacles in implementing autonomy for such systems by answering a technical question: What level of autonomy is needed in military robots and how and when might it be achieved, followed by a techno-legal one: How to implement the rules of humanitarian law within autonomous fighting robots, in order to allow their legal deployment? The first chapter scrutinizes the significance of autonomy in robots and the metrics used to quantify it, which were developed by the US Department of Defense. The second chapter focuses on the autonomy of state-of-the-art” robots (e.g.; Google’s self-driving car, DARPA’s projects, etc.) for navigation, ISR or lethal missions. Based on public information, we will get a hint of the architectures, the functioning, the thresholds and technical limitations of such systems. The bottleneck to a higher autonomy of robots seems to be their poor “perceptive intelligence.” The last chapter looks to the requirements of humanitarian law (rules of “jus in bello”/rules of engagement) to the legal deployment of autonomous lethal robots on the battlefields. The legal and moral reasoning of human soldiers, complying with humanitarian law, is a complex cognitive process which must be emulated by autonomous robots that could make lethal decisions. However, autonomous completion of such “moral” tasks by artificial agents is much more challenging than the autonomous implementation of other tasks, such as navigation, ISR or kinetic attacks. Given the limits of current Artificial Intelligence, it is highly unlikely that robots will acquire such moral capabilities anytime soon. Therefore, for the time being, the autonomous weapon systems might be legally deployed, but only in very particular circumstances, where the requirements of humanitarian law happen to be irrelevant