Automatic symbolic modelling of co-evolutionarily learned robot skills

Proceeding of: 6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001 Granada, Spain, June 13–15, 2001Evolutionary based learning systems have proven to be very powerful techniques for solving a wide range of tasks, from prediction to optimization. However, in some cases the learned concepts are unreadable for humans. This prevents a deep semantic analysis of what has been really learned by those systems. We present in this paper an alternative to obtain symbolic models from subsymbolic learning. In the first stage, a subsymbolic learning system is applied to a given task. Then, a symbolic classifier is used for automatically generating the symbolic counterpart of the subsymbolic model. We have tested this approach to obtain a symbolic model of a neural network. The neural network defines a simple controller af an autonomous robot. a competitive coevolutive method has been applied in order to learn the right weights of the neural network. The results show that the obtained symbolic model is very accurate in the task of modelling the subsymbolic system, adding to this its readability characteristic

Predicting opponent actions in the RoboSoccer

Proceeding of: IEEE International Conference on Systems, Man, and Cybernetics (SMC-2002), 6-9 Oct. 2002, Hammamet, TunezA very important issue in multi-agent systems is that of adaptability to other agents, be it to cooperate or to compete. In competitive domains, the knowledge about the opponent can give any player a clear advantage. In previous work, we acquired models of another agent (the opponent) based only on the observation of its inputs and outputs (its behavior) by formulating the problem as a classification task. In this paper we extend this previous work to the RoboCup domain. However, we have found that models based on a single classifier have bad accuracy, To solve this problem, In this paper we propose to decompose the learning task into two tasks: learning the action name (i.e. kick or dash) and learning the parameter of that action. By using this hierarchical learning approach accuracy results improve, and at worst, the agent can know what action the opponent will carry out, even if there is no high accuracy on the action parameter.Publicad

Predicting opponent actions by bbservation

In competitive domains, the knowledge about the opponent can give players a clear advantage. This idea lead us in the past to propose an approach to acquire models of opponents, based only on the observation of their input-output behavior. If opponent outputs could be accessed directly, a model can be constructed by feeding a machine learning method with traces of the opponent. However, that is not the case in the Robocup domain. To overcome this problem, in this paper we present a three phases approach to model low-level behavior of individual opponent agents. First, we build a classifier to label opponent actions based on observation. Second, our agent observes an opponent and labels its actions using the previous classifier. From these observations, a model is constructed to predict the opponent actions. Finally, the agent uses the model to anticipate opponent reactions. In this paper, we have presented a proof-of-principle of our approach, termed OMBO (Opponent Modeling Based on Observation), so that a striker agent can anticipate a goalie. Results show that scores are significantly higher using the acquired opponentrsquos model of actions.Publicad

OMBO: An opponent modeling approach

In competitive domains, some knowledge about the opponent can give players a clear advantage. This idea led many people to propose approaches that automatically acquire models of opponents, based only on the observation of their input–output behavior. If opponent outputs could be accessed directly, a model can be constructed by feeding a machine learning method with traces of the behavior of the opponent. However, that is not the case in the RoboCup domain where an agent does not have direct access to the opponent inputs and outputs. Rather, the agent sees the opponent behavior from its own point of view and inputs and outputs (actions) have to be inferred from observation. In this paper, we present an approach to model low-level behavior of individual opponent agents. First, we build a classifier to infer and label opponent actions based on observation. Second, our agent observes an opponent and labels its actions using the previous classifier. From these observations, machine learning techniques generate a model that predicts the opponent actions. Finally, the agent uses the model to anticipate opponent actions. In order to test our ideas, we have created an architecture called OMBO (Opponent Modeling Based on Observation). Using OMBO, a striker agent can anticipate goalie actions. Results show that in this striker-goalie scenario, scores are significantly higher using the acquired opponent's model of actions.This work has been partially supported by the Spanish MCyT under projects TRA2007-67374- C02-02 and TIN-2005-08818-C04.Also, it has been supported under MEC grant by TIN2005-08945- C06-05. We thank anonymous reviewers for their helpful comments.Publicad

A Posture Sequence Learning System for an Anthropomorphic Robotic Hand

The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with a human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence

Whole brain Probabilistic Generative Model toward Realizing Cognitive Architecture for Developmental Robots

Building a humanlike integrative artificial cognitive system, that is, an artificial general intelligence, is one of the goals in artificial intelligence and developmental robotics. Furthermore, a computational model that enables an artificial cognitive system to achieve cognitive development will be an excellent reference for brain and cognitive science. This paper describes the development of a cognitive architecture using probabilistic generative models (PGMs) to fully mirror the human cognitive system. The integrative model is called a whole-brain PGM (WB-PGM). It is both brain-inspired and PGMbased. In this paper, the process of building the WB-PGM and learning from the human brain to build cognitive architectures is described.Comment: 55 pages, 8 figures, submitted to Neural Network

The Origins of Self

The Origins of Self explores the role that selfhood plays in defining human society, and each human individual in that society. It considers the genetic and cultural origins of self, the role that self plays in socialisation and language, and the types of self we generate in our individual journeys to and through adulthood. Edwardes argues that other awareness is a relatively early evolutionary development, present throughout the primate clade and perhaps beyond, but self-awareness is a product of the sharing of social models, something only humans appear to do. The self of which we are aware is not something innate within us, it is a model of our self produced as a response to the models of us offered to us by other people. Edwardes proposes that human construction of selfhood involves seven different types of self. All but one of them are internally generated models, and the only non-model, the actual self, is completely hidden from conscious awareness. We rely on others to tell us about our self, and even to let us know we are a self

Precis of neuroconstructivism: how the brain constructs cognition

Neuroconstructivism: How the Brain Constructs Cognition proposes a unifying framework for the study of cognitive development that brings together (1) constructivism (which views development as the progressive elaboration of increasingly complex structures), (2) cognitive neuroscience (which aims to understand the neural mechanisms underlying behavior), and (3) computational modeling (which proposes formal and explicit specifications of information processing). The guiding principle of our approach is context dependence, within and (in contrast to Marr [1982]) between levels of organization. We propose that three mechanisms guide the emergence of representations: competition, cooperation, and chronotopy; which themselves allow for two central processes: proactivity and progressive specialization. We suggest that the main outcome of development is partial representations, distributed across distinct functional circuits. This framework is derived by examining development at the level of single neurons, brain systems, and whole organisms. We use the terms encellment, embrainment, and embodiment to describe the higher-level contextual influences that act at each of these levels of organization. To illustrate these mechanisms in operation we provide case studies in early visual perception, infant habituation, phonological development, and object representations in infancy. Three further case studies are concerned with interactions between levels of explanation: social development, atypical development and within that, developmental dyslexia. We conclude that cognitive development arises from a dynamic, contextual change in embodied neural structures leading to partial representations across multiple brain regions and timescales, in response to proactively specified physical and social environment

Where is cognition? Towards an embodied, situated, and distributed interactionist theory of cognitive activity

In recent years researchers from a variety of cognitive science disciplines have begun to challenge some of the core assumptions of the dominant theoretical framework of cognitivism including the representation-computational view of cognition, the sense-model-plan-act understanding of cognitive architecture, and the use of a formal task description strategy for investigating the organisation of internal mental processes. Challenges to these assumptions are illustrated using empirical findings and theoretical arguments from the fields such as situated robotics, dynamical systems approaches to cognition, situated action and distributed cognition research, and sociohistorical studies of cognitive development. Several shared themes are extracted from the findings in these research programmes including: a focus on agent-environment systems as the primary unit of analysis; an attention to agent-environment interaction dynamics; a vision of the cognizer's internal mechanisms as essentially reactive and decentralised in nature; and a tendency for mutual definitions of agent, environment, and activity. It is argued that, taken together, these themes signal the emergence of a new approach to cognition called embodied, situated, and distributed interactionism. This interactionist alternative has many resonances with the dynamical systems approach to cognition. However, this approach does not provide a theory of the implementing substrate sufficient for an interactionist theoretical framework. It is suggested that such a theory can be found in a view of animals as autonomous systems coupled with a portrayal of the nervous system as a regulatory, coordinative, and integrative bodily subsystem. Although a number of recent simulations show connectionism's promise as a computational technique in simulating the role of the nervous system from an interactionist perspective, this embodied connectionist framework does not lend itself to understanding the advanced 'representation hungry' cognition we witness in much human behaviour. It is argued that this problem can be solved by understanding advanced cognition as the re-use of basic perception-action skills and structures that this feat is enabled by a general education within a social symbol-using environment