Conceptually founded on the "patternist" systems theory of intelligence outlined in (Goertzel 2006), OCP combines Defining Forgetting In ordinary human discourse, the multiple AI paradigms such as uncertain logic, computational word "forget" has multiple shades of meaning. It can refer linguistics, evolutionary program learning and connectionist to the irreversible elimination of a certain knowledge item attention allocation in a unified architecture. Cognitive from memory; or it can mean something milder, as in cases processes embodying these different paradigms interoperate where someone "forgets" something, but then remembers it together on a common neural-symbolic knowledge shortly after. In the latter case, "forgetting" means that the store called the Atomspace. The interaction of these processes knowledge item has been stored in some portion of memory is designed to encourage the self-organizing emergence from which access is slow and uncertain.

The robotics domain is challenging from a learning perspective, since subsequent observations are dependent and the environment is typically non-stationary. Successful modeling of sensorimotor relationships therefore necessitates an open-ended learning process that continuously updates existing models when novel observations become available, while at the same time respecting strict timing constraints. These requirements can be met by combining standard Bayesian regression with an exact update rule for incremental operation and a kernel approximation for non-linearity. The resulting method is characterized by a constant update complexity, which effectively allows lifelong operation. Furthermore, an experimental validation on predicting inverse dynamics of the iCub humanoid demonstrates superior generalization and timing performance with respect to competitive methods.

When a new-born animal (agent) opens its eyes, what it sees is a patchwork of light and dark patterns, the natural scene.What is perceived by the agent at this moment is based on the patternof neural spikes in its brain. Life-long learning begins with such a flood of spikes in the brain. All knowledge and skills learned by the agent are mediated by such spikes, thus it is critical to understand what information these spikes convey and how they can be used to generate meaningful behavior. Here, we consider how agents can autonomously understand the meaning of these spikes without direct reference to the stimulus. We find that this problem, the problem of grounding, is unsolvable if the agent is passively perceiving, and that it can be solved only through self-initiated action. Furthermore, we show that a simple criterion, combined with standard reinforcement learning, can help solve this problem. We will present simulation results and discuss the implications of these results on life-long learning.

Autonomous robots demand complex behavior to deal with unstructured environments. To meet these expectations, a robot needs to address a suite of problems associated with long term knowledge acquisition, representation, and execution in the presence of partial information. In this paper, we address these issues by the acquisition of broad, domain general skills using an intrinsically motivated reward function. We show how these skills can be represented compactly and used hierarchically to obtain complex manipulation skills. We further present a Bayesian model using the learned skills to model objects in the world, in terms of the actions they afford. We argue that our knowledge representation allows a robot to both predict the dynamics of objects in the world as well as recognize them.

In 2009, an average American spent 3 hours per day watching TV. Recent advances in TV entertainment technologies, such as on-demand content, browsing the Internet, and 3D displays, have changed the traditional role of the TV and turned it into the center of home entertainment. Most of these technologies are personal and would benefit from seamless identification of who sits in front of the TV. In this work, we propose a practical and highly accurate solution to this problem. This solution uses a camera, which is mounted on a TV, to recognize faces of people in front of the TV. To make the approach practical, we employ online learning on graphs and show that we can learn highly accurate face models in difficult circumstances from as little as one labeled example. To evaluate our solutions, we collected a 10-hour long dataset of 8 people who watch TV. Our precision and recall are in the upper nineties, and show the promise of utilizing our approach in an embedded setting.

Recently, information gain has been proposed as a candidate intrinsic motivation for lifelong learning agents that may not always have a specific task.  In the InfoMax control framework, reinforcement learning is used to find a control policy for a POMDP in which movement and sensing actions are selected to reduce Shannon entropy as quickly as possible. In this study, we implement InfoMax control on a robot which can move between objects and perform sound-producing manipulations on them.  We formulate a novel latent variable mixture model for acoustic similarities and learn InfoMax polices that allow the robot to rapidly reduce uncertainty about the categories of the objects in a room. We find that InfoMax with our improved acoustic model leads to policies which lead to high classification accuracy.  Interestingly, we also find that with an insufficient model, the InfoMax policy eventually learns to "bury its head in the sand" to avoid getting additional evidence that might increase uncertainty.  We discuss the implications of this finding for InfoMax as a principle of intrinsic motivation in lifelong learning agents.

Skill discovery algorithms in reinforcement learning typically identify single states or regions in state space that correspond to potential task-specific subgoals. However, such methods do not directly address the question of how many distinct skills are appropriate for solving the tasks that the agent faces. This can be highly inefficient when many identified subgoals correspond to the same underlying skill, but are all used in- dividually as skill goals. Furthermore, skills created in this manner are often only transferable to tasks that share iden- tical state spaces, since corresponding subgoals across tasks are not merged into a single skill goal. We show that these problems can be overcome by clustering subgoal data defined in an agent-space and using the resulting clusters as templates for skill termination conditions. Clustering via a Dirichlet process mixture model is used to discover a minimal, suffi- cient collection of portable skills.

Versatile agents situated in rich, dynamic environments must It is not necessarily possible to select the source knowledge be capable of continually learning and refining their knowledge to transfer to a new target task by examining only the surface through experience. These agents will face a variety of similarities between the tasks. The selection must support learning tasks, and can transfer knowledge between tasks to the process of knowledge transfer by choosing source improve performance and accelerate learning. In this context, knowledge based on whether it will transfer well to the target a learning task can be as simple as discovering the effects task. In our previous work, we developed methods that of an operator on the environment, or as complex as accomplishing identify the source knowledge to transfer based on this concept a specific goal -- anything that can be learned of transferability to the target task. Intuitively, transferability can be considered a task. As the agent experiences and learns is the amount that the transferred information is a model for each task, it gains access to new data and knowledge.

One kind of meaningful structures in indoor rooms are supporting structures like tables and cupboards. A robot will need to know these structures for a natural interaction with the human and the environment. As bottom-up detection of such structures is a challenging problem, we propose to estimate potential supporting structures from a spatial description like ``a bowl on the table''. As language and cognition schematize the space in the same way it is possible to estimate the representation of the space underlying a scene description. To do so, we introduce the aligned modeling approach which consists of rules transforming a sequence of object relations into a set of trees and a methodology to ground the abstract representation of the scene layout in the current perception using detectors for small movable objects and an extraction of planar surfaces. An analysis of 30 descriptions shows the robustness of our approach to a variety of description strategies and object detection errors.

At a computational level, language processing tasks are traditionally processed in a language-only space/context, isolated from perception and action. However, at a cognitive level, language processing has been shown experimentally to be embodied, i.e. to inform and be informed by perception and action. In this paper, we argue that embodied cognition dictates the development of a new generation of language processing tools that bridge the gap between the symbolic and the sensorimotor representation spaces. We describe that tasks and challenges such tools need to address and provide an overview of the first such suite of processing tools developed in the framework of the POETICON project.