The organization of an autonomous learning system

The organization of systems that learn from experience is examined, human beings and animals being prime examples of such systems. How is their information processing organized. They build an internal model of the world and base their actions on the model. The model is dynamic and predictive, and it includes the systems' own actions and their effects. In modeling such systems, a large pattern of features represents a moment of the system's experience. Some of the features are provided by the system's senses, some control the system's motors, and the rest have no immediate external significance. A sequence of such patterns then represents the system's experience over time. By storing such sequences appropriately in memory, the system builds a world model based on experience. In addition to the essential function of memory, fundamental roles are played by a sensory system that makes raw information about the world suitable for memory storage and by a motor system that affects the world. The relation of sensory and motor systems to the memory is discussed, together with how favorable actions can be learned and unfavorable actions can be avoided. Results in classical learning theory are explained in terms of the model, more advanced forms of learning are discussed, and the relevance of the model to the frame problem of robotics is examined

An integrated probabilistic framework for robot perception, learning and memory

Learning and perception from multiple sensory modalities are crucial processes for the development of intelligent systems capable of interacting with humans. We present an integrated probabilistic framework for perception, learning and memory in robotics. The core component of our framework is a computational Synthetic Autobiographical Memory model which uses Gaussian Processes as a foundation and mimics the functionalities of human memory. Our memory model, that operates via a principled Bayesian probabilistic framework, is capable of receiving and integrating data flows from multiple sensory modalities, which are combined to improve perception and understanding of the surrounding environment. To validate the model, we implemented our framework in the iCub humanoid robotic, which was able to learn and recognise human faces, arm movements and touch gestures through interaction with people. Results demonstrate the flexibility of our method to successfully integrate multiple sensory inputs, for accurate learning and recognition. Thus, our integrated probabilistic framework offers a promising core technology for robust intelligent systems, which are able to perceive, learn and interact with people and their environments

Exploiting linked data to create rich human digital memories

Memories are an important aspect of a person's life and experiences. The area of human digital memories focuses on encapsulating this phenomenon, in a digital format, over a lifetime. Through the proliferation of ubiquitous devices, both people and the surrounding environment are generating a phenomenal amount of data. With all of this disjointed information available, successfully searching it and bringing it together, to form a human digital memory, is a challenge. This is especially true when a lifetime of data is being examined. Linked Data provides an ideal, and novel, solution for overcoming this challenge, where a variety of data sources can be drawn upon to capture detailed information surrounding a given event. Memories, created in this way, contain vivid structures and varied data sources, which emerge through the semantic clustering of content and other memories. This paper presents DigMem, a platform for creating human digital memories, based on device-specific services and the user's current environment. In this way, information is semantically structured to create temporal "memory boxes" for human experiences. A working prototype has been successfully developed, which demonstrates the approach. In order to evaluate the applicability of the system a number of experiments have been undertaken. These have been successful in creating human digital memories and illustrating how a user can be monitored in both indoor and outdoor environments. Furthermore, the user's heartbeat information is analysed to determine his or her heart rate. This has been achieved with the development of a QRS Complex detection algorithm and heart rate calculation method. These methods process collected electrocardiography (ECG) information to discern the heart rate of the user

Machine Learning Tools for Optimization of Fuel Consumption at Signalized Intersections in Connected/Automated Vehicles Environment

Researchers continue to seek numerous techniques for making the transportation sector more sustainable in terms of fuel consumption and greenhouse gas emissions. Among the most effective techniques is Eco-driving at signalized intersections. Eco-driving is a complex control problem where drivers approaching the intersections are guided, over a period of time, to optimize fuel consumption. Eco-driving control systems reduce fuel consumption by optimizing vehicle trajectories near signalized intersections based on information of the SpaT (Signal Phase and Timing). Developing Eco-driving applications for semi-actuated signals, unlike pre-timed, is more challenging due to variations in cycle length resulting from fluctuations in traffic demand. Reinforcement learning (RL) is a machine learning paradigm that mimics the human learning behavior where an agent attempts to solve a given control problem by interacting with the environment and developing an optimal policy. Unlike the methods implemented in previous studies for solving the Eco-driving problem, RL does not necessitate prior knowledge of the environment being learned and processed. Therefore, the aim of this study is twofold: (1) Develop a novel brute force Eco-driving algorithm (ECO-SEMI-Q) for CAV (Connected/Autonomous Vehicles) passing through semi-actuated signalized intersections; and (2) Develop a novel Deep Reinforcement Learning (DRL) Eco-driving algorithm for CAV passing through fixed-time signalized intersections. The developed algorithms are tested at both microscopic and macroscopic levels. For the microscopic level, results indicate that the fuel consumption for vehicles controlled by the ECO-SEMI-Q and DRL models is 29.2% and 23% less than that for the case with no control, respectively. For the macroscopic level, a sensitivity analysis for the impact of MPR (Market Penetration Rate) shows that the savings in fuel consumption increase with higher MPR. Furthermore, when MPR is greater than 50%, the ECO-SEMI-Q algorithm provides appreciable savings in travel times. The sensitivity analysis indicates savings in the network fuel consumption when the MPR of the DRL algorithm is higher than 35%. At MPR less than 35%, the DRL algorithm has an adverse impact on fuel consumption due to aggressive lane change and passing maneuvers. These reductions in fuel consumption demonstrate the ability of the algorithms to provide more environmentally sustainable signalized intersections

An incremental clustering and associative learning architecture for intelligent robotics

The ability to learn from the environment and memorise the acquired knowledge is essential for robots to become autonomous and versatile artificial companions. This thesis proposes a novel learning and memory architecture for robots, which performs associative learning and recall of sensory and actuator patterns. The approach avoids the inclusion of task-specific expert knowledge and can deal with any kind of multi-dimensional real-valued data, apart from being tolerant to noise and supporting incremental learning. The proposed architecture integrates two machine learning methods: a topology learning algorithm that performs incremental clustering, and an associative memory model that learns relationship information based on the co-occurrence of inputs. The evaluations of both the topology learning algorithm and the associative memory model involved the memorisation of high-dimensional visual data as well as the association of symbolic data, presented simultaneously and sequentially. Moreover, the document analyses the results of two experiments in which the entire architecture was evaluated regarding its associative and incremental learning capabilities. One experiment comprised an incremental learning task with visual patterns and text labels, which was performed both in a simulated scenario and with a real robot. In a second experiment a robot learned to recognise visual patterns in the form of road signs and associated them with di erent con gurations of its arm joints. The thesis also discusses several learning-related aspects of the architecture and highlights strengths and weaknesses of the proposed approach. The developed architecture and corresponding ndings contribute to the domains of machine learning and intelligent robotics

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

Lessons and new directions for extended cognition from social and personality psychology

This paper aims to expand the range of empirical work relevant to the extended cognition debates. First, I trace the historical development of the person-situation debate in social and personality psychology and the extended cognition debate in the philosophy of mind. Next, I highlight some instructive similarities between the two and consider possible objections to my comparison. I then argue that the resolution of the person-situation debate in terms of interactionism lends support for an analogously interactionist conception of extended cognition. I argue that this interactionism might necessitate a shift away from the dominant agent-artifact paradigm toward an agent–agent paradigm. If this is right, then social and personality psychology—the discipline(s) that developed from the person-situation debate—opens a whole new range of empirical considerations for extended cognition theorists which align with Clark & Chalmers original vision of agents themselves as spread into the world