LIDA: A Working Model of Cognition

In this paper we present the LIDA architecture as a working model of cognition. We argue that such working models are broad in scope and address real world problems in comparison to experimentally based models which focus on specific pieces of cognition. While experimentally based models are useful, we need a working model of cognition that integrates what we know from neuroscience, cognitive science and AI. The LIDA architecture provides such a working model. A LIDA based cognitive robot or software agent will be capable of multiple learning mechanisms. With artificial feelings and emotions as primary motivators and learning facilitators, such systems will ‘live’ through a developmental period during which they will learn in multiple ways to act in an effective, human-like manner in complex, dynamic, and unpredictable environments. We discuss the integration of the learning mechanisms into the existing IDA architecture as a working model of cognition

A Cognitive Science Based Machine Learning Architecture

In an attempt to illustrate the application of cognitive science principles to hard AI problems in machine learning we propose the LIDA technology, a cognitive science based architecture capable of more human-like learning. A LIDA based software agent or cognitive robot will be capable of three fundamental, continuously active, humanlike learning mechanisms:\ud 1) perceptual learning, the learning of new objects, categories, relations, etc.,\ud 2) episodic learning of events, the what, where, and when,\ud 3) procedural learning, the learning of new actions and action sequences with which to accomplish new tasks. The paper argues for the use of modular components, each specializing in implementing individual facets of human and animal cognition, as a viable approach towards achieving general intelligence

Prospection in cognition: the case for joint episodic-procedural memory in cognitive robotics

Prospection lies at the core of cognition: it is the means by which an agent \u2013 a person or a cognitive robot \u2013 shifts its perspective from immediate sensory experience to anticipate future events, be they the actions of other agents or the outcome of its own actions. Prospection, accomplished by internal simulation, requires mechanisms for both perceptual imagery and motor imagery. While it is known that these two forms of imagery are tightly entwined in the mirror neuron system, we do not yet have an effective model of the mentalizing network which would provide a framework to integrate declarative episodic and procedural memory systems and to combine experiential knowledge with skillful know-how. Such a framework would be founded on joint perceptuo-motor representations. In this paper, we examine the case for this form of representation, contrasting sensory-motor theory with ideo-motor theory, and we discuss how such a framework could be realized by joint episodic-procedural memory. We argue that such a representation framework has several advantages for cognitive robots. Since episodic memory operates by recombining imperfectly recalled past experience, this allows it to simulate new or unexpected events. Furthermore, by virtue of its associative nature, joint episodic-procedural memory allows the internal simulation to be conditioned by current context, semantic memory, and the agent\u2019s value system. Context and semantics constrain the combinatorial explosion of potential perception-action associations and allow effective action selection in the pursuit of goals, while the value system provides the motives that underpin the agent\u2019s autonomy and cognitive development. This joint episodic-procedural memory framework is neutral regarding the final implementation of these episodic and procedural memories, which can be configured sub-symbolically as associative networks or symbolically as content-addressable image databases and databases of motor-control scripts

Motivations, Values and Emotions: 3 sides of the same coin

This position paper speaks to the interrelationships between the three concepts of motivations, values, and emotion. Motivations prime actions, values serve to choose between motivations, emotions provide a common currency for values, and emotions implement motivations. While conceptually distinct, the three are so pragmatically intertwined as to differ primarily from our taking different points of view. To make these points more transparent, we briefly describe the three in the context a cognitive architecture, the LIDA model, for software agents and robots that models human cognition, including a developmental period. We also compare the LIDA model with other models of cognition, some involving learning and emotions. Finally, we conclude that artificial emotions will prove most valuable as implementers of motivations in situations requiring learning and development

Joint Goal Human Robot collaboration-From Remembering to Inferring

The ability to infer goals, consequences of one’s own and others’ actions is a critical desirable feature for robots to truly become our companions-thereby opening up applications in several domains. This article proposes the viewpoint that the ability to remember our own past experiences based on present context enables us to infer future consequences of both our actions/goals and observed actions/goals of the other (by analogy). In this context, a biomimetic episodic memory architecture to encode diverse learning experiences of iCub humanoid is presented. The critical feature is that partial cues from the present environment like objects perceived or observed actions of a human triggers a recall of context relevant past experiences thereby enabling the robot to infer rewarding future states and engage in cooperative goal-oriented behaviors. An assembly task jointly done by human and the iCub humanoid is used to illustrate the framework. Link between the proposed framework and emerging results from neurosciences related to shared cortical basis for ‘remembering, imagining and perspective taking’ is discussed

Machines Learning - Towards a New Synthetic Autobiographical Memory

Autobiographical memory is the organisation of episodes and contextual information from an individual’s experiences into a coherent narrative, which is key to our sense of self. Formation and recall of autobiographical memories is essential for effective, adaptive behaviour in the world, providing contextual information necessary for planning actions and memory functions such as event reconstruction. A synthetic autobiographical memory system would endow intelligent robotic agents with many essential components of cognition through active compression and storage of historical sensorimotor data in an easily addressable manner. Current approaches neither fulfil these functional requirements, nor build upon recent understanding of predictive coding, deep learning, nor the neurobiology of memory. This position paper highlights desiderata for a modern implementation of synthetic autobiographical memory based on human episodic memory, and proposes that a recently developed model of hippocampal memory could be extended as a generalised model of autobiographical memory. Initial implementation will be targeted at social interaction, where current synthetic autobiographical memory systems have had success

Memory and mental time travel in humans and social robots.

From neuroscience, brain imaging and the psychology of memory, we are beginning to assemble an integrated theory of the brain subsystems and pathways that allow the compression, storage and reconstruction of memories for past events and their use in contextualizing the present and reasoning about the future-mental time travel (MTT). Using computational models, embedded in humanoid robots, we are seeking to test the sufficiency of this theoretical account and to evaluate the usefulness of brain-inspired memory systems for social robots. In this contribution, we describe the use of machine learning techniques-Gaussian process latent variable models-to build a multimodal memory system for the iCub humanoid robot and summarize results of the deployment of this system for human-robot interaction. We also outline the further steps required to create a more complete robotic implementation of human-like autobiographical memory and MTT. We propose that generative memory models, such as those that form the core of our robot memory system, can provide a solution to the symbol grounding problem in embodied artificial intelligence. This article is part of the theme issue 'From social brains to social robots: applying neurocognitive insights to human-robot interaction'.Funding. The preparation of this chapter was supported by funding from the EU Seventh Framework Programme as part of the projects Experimental Functional Android Assistant (EFAA, FP7-ICT-270490) and What You Say Is What You Did (WYSIWYD, FP7-ICT-612139) and by the EU H2020 Programme as part of the Human Brain Project (HBP-SGA1, 720270; HBP-SGA2, 785907). Acknowledgements. The authors are grateful to Paul Verschure, Peter Dominey, Giorgio Metta, Yiannis Demiris and the other members of the WYSIWYD and EFAA consortia; to members of the HBP EPISENSE group; and to our colleagues at the University of Sheffield who have helped us to develop memory systems for the iCub, particularly Luke Boorman, Harry Jackson and Matthew Evans. The Sheffield iCub was purchased with the support of the UK Engineering and Physical Sciences Research Council (EPSRC)

A view of Kanerva's sparse distributed memory

Pentti Kanerva is working on a new class of computers, which are called pattern computers. Pattern computers may close the gap between capabilities of biological organisms to recognize and act on patterns (visual, auditory, tactile, or olfactory) and capabilities of modern computers. Combinations of numeric, symbolic, and pattern computers may one day be capable of sustaining robots. The overview of the requirements for a pattern computer, a summary of Kanerva's Sparse Distributed Memory (SDM), and examples of tasks this computer can be expected to perform well are given

Integer Sparse Distributed Memory and Modular Composite Representation

Challenging AI applications, such as cognitive architectures, natural language understanding, and visual object recognition share some basic operations including pattern recognition, sequence learning, clustering, and association of related data. Both the representations used and the structure of a system significantly influence which tasks and problems are most readily supported. A memory model and a representation that facilitate these basic tasks would greatly improve the performance of these challenging AI applications.Sparse Distributed Memory (SDM), based on large binary vectors, has several desirable properties: auto-associativity, content addressability, distributed storage, robustness over noisy inputs that would facilitate the implementation of challenging AI applications. Here I introduce two variations on the original SDM, the Extended SDM and the Integer SDM, that significantly improve these desirable properties, as well as a new form of reduced description representation named MCR.Extended SDM, which uses word vectors of larger size than address vectors, enhances its hetero-associativity, improving the storage of sequences of vectors, as well as of other data structures. A novel sequence learning mechanism is introduced, and several experiments demonstrate the capacity and sequence learning capability of this memory.Integer SDM uses modular integer vectors rather than binary vectors, improving the representation capabilities of the memory and its noise robustness. Several experiments show its capacity and noise robustness. Theoretical analyses of its capacity and fidelity are also presented.A reduced description represents a whole hierarchy using a single high-dimensional vector, which can recover individual items and directly be used for complex calculations and procedures, such as making analogies. Furthermore, the hierarchy can be reconstructed from the single vector. Modular Composite Representation (MCR), a new reduced description model for the representation used in challenging AI applications, provides an attractive tradeoff between expressiveness and simplicity of operations. A theoretical analysis of its noise robustness, several experiments, and comparisons with similar models are presented.My implementations of these memories include an object oriented version using a RAM cache, a version for distributed and multi-threading execution, and a GPU version for fast vector processing