Structural Bootstrapping - A Novel, Generative Mechanism for Faster and More Efficient Acquisition of Action-Knowledge

eISSN: 1943-0612Humans, but also robots, learn to improve their behavior. Without existing knowledge, learning either needs to be explorative and, thus, slow or-to be more efficient-it needs to rely on supervision, which may not always be available. However, once some knowledge base exists an agent can make use of it to improve learning efficiency and speed. This happens for our children at the age of around three when they very quickly begin to assimilate new information by making guided guesses how this fits to their prior knowledge. This is a very efficient generative learning mechanism in the sense that the existing knowledge is generalized into as-yet unexplored, novel domains. So far generative learning has not been employed for robots and robot learning remains to be a slow and tedious process. The goal of the current study is to devise for the first time a general framework for a generative process that will improve learning and which can be applied at all different levels of the robot's cognitive architecture. To this end, we introduce the concept of structural bootstrapping-borrowed and modified from child language acquisition-to define a probabilistic process that uses existing knowledge together with new observations to supplement our robot's data-base with missing information about planning-, object-, as well as, action-relevant entities. In a kitchen scenario, we use the example of making batter by pouring and mixing two components and show that the agent can efficiently acquire new knowledge about planning operators, objects as well as required motor pattern for stirring by structural bootstrapping. Some benchmarks are shown, too, that demonstrate how structural bootstrapping improves performanceTaikomosios informatikos katedraVytauto Didžiojo universiteta

Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

Developmental Bootstrapping of AIs

Although some current AIs surpass human abilities in closed artificial worlds such as board games, their abilities in the real world are limited. They make strange mistakes and do not notice them. They cannot be instructed easily, fail to use common sense, and lack curiosity. They do not make good collaborators. Mainstream approaches for creating AIs are the traditional manually-constructed symbolic AI approach and generative and deep learning AI approaches including large language models (LLMs). These systems are not well suited for creating robust and trustworthy AIs. Although it is outside of the mainstream, the developmental bootstrapping approach has more potential. In developmental bootstrapping, AIs develop competences like human children do. They start with innate competences. They interact with the environment and learn from their interactions. They incrementally extend their innate competences with self-developed competences. They interact and learn from people and establish perceptual, cognitive, and common grounding. They acquire the competences they need through bootstrapping. However, developmental robotics has not yet produced AIs with robust adult-level competences. Projects have typically stopped at the Toddler Barrier corresponding to human infant development at about two years of age, before their speech is fluent. They also do not bridge the Reading Barrier, to skillfully and skeptically draw on the socially developed information resources that power current LLMs. The next competences in human cognitive development involve intrinsic motivation, imitation learning, imagination, coordination, and communication. This position paper lays out the logic, prospects, gaps, and challenges for extending the practice of developmental bootstrapping to acquire further competences and create robust, resilient, and human-compatible AIs.Comment: 102 pages, 29 figure

Bootstrapping Relational Affordances of Object Pairs using Transfer

This work was supported in part by the U.K. EPSRC DTG EP/J5000343/1 at Aberdeen, and in part by the EU Cognitive Systems Project XPERIENCE at SDU under Grant FP7-ICT-270273.Peer reviewedPostprin

Sequencing in SLA: Phonological Memory, Chunking and Points of Order.

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139863/1/Ellis1996Chunking.pd

Continual Lifelong Learning with Neural Networks: A Review

Humans and animals have the ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. This ability, referred to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms that together contribute to the development and specialization of our sensorimotor skills as well as to long-term memory consolidation and retrieval. Consequently, lifelong learning capabilities are crucial for autonomous agents interacting in the real world and processing continuous streams of information. However, lifelong learning remains a long-standing challenge for machine learning and neural network models since the continual acquisition of incrementally available information from non-stationary data distributions generally leads to catastrophic forgetting or interference. This limitation represents a major drawback for state-of-the-art deep neural network models that typically learn representations from stationary batches of training data, thus without accounting for situations in which information becomes incrementally available over time. In this review, we critically summarize the main challenges linked to lifelong learning for artificial learning systems and compare existing neural network approaches that alleviate, to different extents, catastrophic forgetting. We discuss well-established and emerging research motivated by lifelong learning factors in biological systems such as structural plasticity, memory replay, curriculum and transfer learning, intrinsic motivation, and multisensory integration

Learning Functional Prepositions

In first language acquisition, what does it mean for a grammatical category to have been acquired, and what are the mechanisms by which children learn functional categories in general? In the context of prepositions (Ps), if the lexical/functional divide cuts through the P category, as has been suggested in the theoretical literature, then constructivist accounts of language acquisition would predict that children develop adult-like competence with the more abstract units, functional Ps, at a slower rate compared to their acquisition of lexical Ps. Nativists instead assume that the features of functional P are made available by Universal Grammar (UG), and are mapped as quickly, if not faster, than the semantic features of their lexical counterparts. Conversely, if Ps are either all lexical or all functional, on both accounts of acquisition we should observe few differences in learning. Three empirical studies of the development of P were conducted via computer analysis of the English and Spanish sub-corpora of the CHILDES database. Study 1 analyzed errors in child usage of Ps, finding almost no errors in commission in either language, but that the English learners lag in their production of functional Ps relative to lexical Ps. That no such delay was found in the Spanish data suggests that the English pattern is not universal. Studies 2 and 3 applied novel measures of phrasal (P head + nominal complement) productivity to the data. Study 2 examined prepositional phrases (PPs) whose head-complement pairs appeared in both child and adult speech, while Study 3 considered PPs produced by children that never occurred in adult speech. In both studies the productivity of Ps for English children developed faster than that of lexical Ps. In Spanish there were few differences, suggesting that children had already mastered both orders of Ps early in acquisition. These empirical results suggest that at least in English P is indeed a split category, and that children acquire the syntax of the functional subset very quickly, committing almost no errors. The UG position is thus supported. Next, the dissertation investigates a \u27soft nativist\u27 acquisition strategy that composes the distributional analysis of input, minimal a priori knowledge of the possible co-occurrence of morphosyntactic features associated with functional elements, and linguistic knowledge that is presumably acquired via the experience of pragmatic, communicative situations. The output of the analysis consists in a mapping of morphemes to the feature bundles of nominative pronouns for English and Spanish, plus specific claims about the sort of knowledge required from experience. The acquisition model is then extended to adpositions, to examine what, if anything, distributional analysis can tell us about the functional sequences of PPs. The results confirm the theoretical position according to which spatiotemporal Ps are lexical in character, rooting their own extended projections, and that functional Ps express an aspectual sequence in the functional superstructure of the PP

Management learning in business networks:the process and the effects

This article studies the process of firm learning in business networks and its impact on new product development. It argues that prior work offers little insight into how learning actually takes place in network collaboration, and so poses the open question of whether learning through business networks does accelerate new product development. The article begins by clarifying important learning concepts. It then documents how these concepts evolve across organisational boundaries and projects and also over time. It also shows how companies apply the skills of dialogue, articulation and experience for knowledge transfer and how they engage in articulation and pollination for knowledge cross-transformation. However, despite being able to document these processes, this study is unable to unequivocally link knowledge transfer with new product development efficiency and conclude that the latter is enhanced by knowledge cross-transformation. This article contributes to a theoretical inter-organisational learning model in business networks and suggests improved ways for management learning

The Relational Bottleneck as an Inductive Bias for Efficient Abstraction

A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience. This effort has often been framed in terms of a dichotomy between empiricist and nativist approaches, most recently embodied by debates concerning deep neural networks and symbolic cognitive models. Here, we highlight a recently emerging line of work that suggests a novel reconciliation of these approaches, by exploiting an inductive bias that we term the relational bottleneck. We review a family of models that employ this approach to induce abstractions in a data-efficient manner, emphasizing their potential as candidate models for the acquisition of abstract concepts in the human mind and brain