Effects of Training Data Variation and Temporal Representation in a QSR-Based Action Prediction System

Understanding of behaviour is a crucial skill for Artificial Intelligence systems expected to interact with external agents – whether other AI systems, or humans, in scenarios involving co-operation, such as domestic robots capable of helping out with household jobs, or disaster relief robots expected to collaborate and lend assistance to others. It is useful for such systems to be able to quickly learn and re-use models and skills in new situations. Our work centres around a behaviourlearning system utilising Qualitative Spatial Relations to lessen the amount of training data required by the system, and to aid generalisation. In this paper, we provide an analysis of the advantages provided to our system by the use of QSRs. We provide a comparison of a variety of machine learning techniques utilising both quantitative and qualitative representations, and show the effects of varying amounts of training data and temporal representations upon the system. The subject of our work is the game of simulated RoboCup Soccer Keepaway. Our results show that employing QSRs provides clear advantages in scenarios where training data is limited, and provides for better generalisation performance in classifiers. In addition, we show that adopting a qualitative representation of time can provide significant performance gains for QSR systems

ToyArchitecture: Unsupervised Learning of Interpretable Models of the World

Research in Artificial Intelligence (AI) has focused mostly on two extremes: either on small improvements in narrow AI domains, or on universal theoretical frameworks which are usually uncomputable, incompatible with theories of biological intelligence, or lack practical implementations. The goal of this work is to combine the main advantages of the two: to follow a big picture view, while providing a particular theory and its implementation. In contrast with purely theoretical approaches, the resulting architecture should be usable in realistic settings, but also form the core of a framework containing all the basic mechanisms, into which it should be easier to integrate additional required functionality. In this paper, we present a novel, purposely simple, and interpretable hierarchical architecture which combines multiple different mechanisms into one system: unsupervised learning of a model of the world, learning the influence of one's own actions on the world, model-based reinforcement learning, hierarchical planning and plan execution, and symbolic/sub-symbolic integration in general. The learned model is stored in the form of hierarchical representations with the following properties: 1) they are increasingly more abstract, but can retain details when needed, and 2) they are easy to manipulate in their local and symbolic-like form, thus also allowing one to observe the learning process at each level of abstraction. On all levels of the system, the representation of the data can be interpreted in both a symbolic and a sub-symbolic manner. This enables the architecture to learn efficiently using sub-symbolic methods and to employ symbolic inference.Comment: Revision: changed the pdftitl

Self-directedness, integration and higher cognition

In this paper I discuss connections between self-directedness, integration and higher cognition. I present a model of self-directedness as a basis for approaching higher cognition from a situated cognition perspective. According to this model increases in sensorimotor complexity create pressure for integrative higher order control and learning processes for acquiring information about the context in which action occurs. This generates complex articulated abstractive information processing, which forms the major basis for higher cognition. I present evidence that indicates that the same integrative characteristics found in lower cognitive process such as motor adaptation are present in a range of higher cognitive process, including conceptual learning. This account helps explain situated cognition phenomena in humans because the integrative processes by which the brain adapts to control interaction are relatively agnostic concerning the source of the structure participating in the process. Thus, from the perspective of the motor control system using a tool is not fundamentally different to simply controlling an arm

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Writing biology with mutant mice: the monstrous potential of post genomic life

Social scientific accounts identified in the biological grammars of early genomics a monstrous reductionism, ‘an example of brute life, the minimalist essence of things’ (Rabinow, 1996, p. 89). Concern about this reductionism focused particularly on its links to modernist notions of control; the possibility of calculating, predicting and intervening in the biological futures of individuals and populations. Yet, the trajectories of the post genomic sciences have not unfolded in this way, challenging scientists involved in the production and integration of complex biological data and the interpretative strategies of social scientists honed in critiquing this reductionism. The post genomic sciences are now proliferating points from which to understand relations in biology, between genes and environments, as well as between species and spaces, opening up future possibilities and different ways of thinking about life. This paper explores the emerging topologies and temporalities of one form of post genomic research, drawing upon ethnographic research on international efforts in functional genomics, which are using mutant mice to understand mammalian gene function. Using vocabularies on the monstrous from Derrida and Haraway, I suggest an alternative conceptualisation of monstrosity within biology, in which the ascendancy of mice in functional genomics acts as a constant supplement to the reductionist grammars of genomics. Rather than searching for the minimalist essence of things, this form of functional genomics has become an exercise in the production and organization of biological surplus and excess, which is experimental, corporeal and affective. The uncertain functioning of monsters in this contexts acts as a generative catalyst for scientists and social scientists, proliferating perspectives from which to listen to and engage with the mutating landscapes, forms of life, and languages of a post genomic biology

Perception as a Dynamic Activation of Relational Matrices

Here we present an experimental model to be applied to the storage and retrieval of information based on an associative information system’s sensory and motor state change data, aiming to represent the dynamics of a dynamic perceptual system. The model and database implementation use a universal information storage structure holding both data and metadata within the same structure. This model is characterized by the emphasis on associative information about the represented system derived from raw data, which are in their turn produced by the associative system’s interactions with the environment. Instead of defining objects using descriptive relations, this model stores relations between occurents where the represented system is not replicated in its various components, but defined by its relations when they occur. This model therefore represents the dynamics and interaction of systems such as human perception, rather than imposing artificial boundaries and qualities. In essence, the model is an alternative to perceptual knowledge accumulation, which, as we show, can be applied to a database design