Computational and Robotic Models of Early Language Development: A Review

We review computational and robotics models of early language learning and development. We first explain why and how these models are used to understand better how children learn language. We argue that they provide concrete theories of language learning as a complex dynamic system, complementing traditional methods in psychology and linguistics. We review different modeling formalisms, grounded in techniques from machine learning and artificial intelligence such as Bayesian and neural network approaches. We then discuss their role in understanding several key mechanisms of language development: cross-situational statistical learning, embodiment, situated social interaction, intrinsically motivated learning, and cultural evolution. We conclude by discussing future challenges for research, including modeling of large-scale empirical data about language acquisition in real-world environments. Keywords: Early language learning, Computational and robotic models, machine learning, development, embodiment, social interaction, intrinsic motivation, self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J. Horst and J. von Koss Torkildsen, Routledg

Alternate state approach to range management in the sagebrush steppe, An

2011 Summer.Includes bibliographical references.Describing and predicting sudden shifts between alternate states in ecosystems is a frontier in ecology with important implications for natural resource management and human well-being. The range profession has recently adopted an approach to land management decision-making based on alternate state theory. The Natural Resource Conservation Service and partners are creating state and transition models (STMs), conceptual models that describe shifts in ecosystems, for many types of land throughout the US. Motivated by this national STM-building effort, this dissertation has two practical objectives: 1) to create data-driven STMs that describe sagebrush steppe ecosystem response to management, and 2) to develop guidelines for STM creation. A third objective grew out of the need to create theoretically accurate STMs: to determine whether spatial and temporal patterns of vegetation in northwest Colorado sagebrush steppe are consistent with predictions of alternate state theory. The first chapter introduces this work with a review of alternate state theory and how it is applied in constructing STMs. I conducted an observational study of sagebrush steppe response to management practices and ecological disturbances on two soil types in the lower Elkhead watershed. The second chapter examines plant species composition as an indicator of alternate states, a test of the current approach to building STMs. The third chapter investigates whether areas with different structure also differ in function, as predicted by alternate state theory. The fourth chapter compares trait-based group composition to species composition as an indicator of alternate states. From these chapters, I conclude that there are large, management-relevant differences in species composition within environmentally similar areas and that many of these differences are related to site history, as would be expected if these represent alternate states. The Indicators of Rangeland Health show that some states defined by species composition are associated with unique processes that may serve as positive feedback mechanisms which maintain alternate states. Relationships between species composition, processes and environmental gradients suggest that environmental variation may make some transitions between states more likely and should be acknowledged in STMs. Multiple-trait based group composition identifies many of the same potential states and transitions as species composition, but is also sensitive to some different management practices. The Indicators of Rangeland Health and plant traits are simple additions to current STM-building methods that can improve and expedite STM creation. In the final chapter, I describe long-term sagebrush steppe dynamics based on 50 years of monitoring data from the upper Elkhead watershed and evaluate evidence for alternate states. Gradual changes in composition after spraying and the steady increase of a non-native grass suggest that this high-elevation sagebrush steppe ecosystem does not experience sudden shifts between alternate states. I conclude that the alternate state approach to range management shows promise for describing management-relevant ecosystem dynamics and organizing current knowledge. Given the equivocal evidence supporting predictions of alternate state theory for Elkhead watershed sagebrush steppe, further research should determine which aspects of alternate state theory must be confirmed to create useful STMs. In addition, long-term monitoring, modeling, and experiments are needed to validate and update models as we learn more about the sagebrush steppe

Minimal model of associative learning for cross-situational lexicon acquisition

An explanation for the acquisition of word-object mappings is the associative learning in a cross-situational scenario. Here we present analytical results of the performance of a simple associative learning algorithm for acquiring a one-to-one mapping between $N$ objects and $N$ words based solely on the co-occurrence between objects and words. In particular, a learning trial in our learning scenario consists of the presentation of $C + 1 < N$ objects together with a target word, which refers to one of the objects in the context. We find that the learning times are distributed exponentially and the learning rates are given by $\ln{[\frac{N(N-1)}{C + (N-1)^{2}}]}$ in the case the $N$ target words are sampled randomly and by $\frac{1}{N} \ln [\frac{N-1}{C}]$ in the case they follow a deterministic presentation sequence. This learning performance is much superior to those exhibited by humans and more realistic learning algorithms in cross-situational experiments. We show that introduction of discrimination limitations using Weber's law and forgetting reduce the performance of the associative algorithm to the human level

Goldilocks Forgetting in Cross-Situational Learning

Given that there is referential uncertainty (noise) when learning words, to what extent can forgetting filter some of that noise out, and be an aid to learning? Using a Cross Situational Learning model we find a U-shaped function of errors indicative of a "Goldilocks" zone of forgetting: an optimum store-loss ratio that is neither too aggressive nor too weak, but just the right amount to produce better learning outcomes. Forgetting acts as a high-pass filter that actively deletes (part of) the referential ambiguity noise, retains intended referents, and effectively amplifies the signal. The model achieves this performance without incorporating any specific cognitive biases of the type proposed in the constraints and principles account, and without any prescribed developmental changes in the underlying learning mechanism. Instead we interpret the model performance as more of a by-product of exposure to input, where the associative strengths in the lexicon grow as a function of linguistic experience in combination with memory limitations. The result adds a mechanistic explanation for the experimental evidence on spaced learning and, more generally, advocates integrating domain-general aspects of cognition, such as memory, into the language acquisition process

Simultaneous Noun and Category Learning via Cross-Situational Statistics

Abstract Previous research shows that people can acquire an impressive number of word-referent pairs after viewing a series of ambiguous trials by accumulating co-occurrence statistics (e.g., Yu &amp; Smith, 2006). The present study extends the cross-situational word learning paradigm, which has previously dealt only with noun acquisition, and shows that humans can concurrently acquire nouns and adjectives (i.e., a natural category with a distinctive, unifying feature). Furthermore, participants are able to learn ad hoc categories of referents consistently cooccurring with a label, while simultaneously learning instance labels. Thus, humans demonstrate an impressive ability to simultaneously apprehend regularities at multiple levels in their environment

A critical period for robust curriculum-based deep reinforcement learning of sequential action in robot arm

Action Contro

Sustaining Working Rangelands: Insights from Rancher Decision Making

Grazed rangeland ecosystems encompass diverse global land resources and are complex social-ecological systems from which society demands both goods (e.g., livestock and forage production) and services (e.g., abundant and high-quality water). Including the ranching community's perceptions, knowledge, and decision-making is essential to advancing the ongoing dialogue to define sustainable working rangelands. We surveyed 507 (33% response rate) California ranchers to gain insight into key factors shaping their decision-making, perspectives on effective management practices and ranching information sources, as well as their concerns. First, we found that variation in ranch structure, management goals, and decision making across California's ranching operations aligns with the call from sustainability science to maintain flexibility at multiple scales to support the suite of economic and ecological services they can provide. The diversity in ranching operations highlights why single-policy and management "panaceas" often fail. Second, the information resources ranchers rely on suggest that sustaining working rangelands will require collaborative, trust-based partnerships focused on achieving both economic and ecological goals. Third, ranchers perceive environmental regulations and government policies-rather than environmental drivers-as the major threats to the future of their operations