9 research outputs found
Toward evolutionary and developmental intelligence
Given the phenomenal advances in artificial intelligence in specific domains like visual object recognition and game playing by deep learning, expectations are rising for building artificial general intelligence (AGI) that can flexibly find solutions in unknown task domains. One approach to AGI is to set up a variety of tasks and design AI agents that perform well in many of them, including those the agent faces for the first time. One caveat for such an approach is that the best performing agent may be just a collection of domain-specific AI agents switched for a given domain. Here we propose an alternative approach of focusing on the process of acquisition of intelligence through active interactions in an environment. We call this approach evolutionary and developmental intelligence (EDI). We first review the current status of artificial intelligence, brain-inspired computing and developmental robotics and define the conceptual framework of EDI. We then explore how we can integrate advances in neuroscience, machine learning, and robotics to construct EDI systems and how building such systems can help us understand animal and human intelligence
SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model
To realize human-like robot intelligence, a large-scale cognitive
architecture is required for robots to understand the environment through a
variety of sensors with which they are equipped. In this paper, we propose a
novel framework named Serket that enables the construction of a large-scale
generative model and its inference easily by connecting sub-modules to allow
the robots to acquire various capabilities through interaction with their
environments and others. We consider that large-scale cognitive models can be
constructed by connecting smaller fundamental models hierarchically while
maintaining their programmatic independence. Moreover, connected modules are
dependent on each other, and parameters are required to be optimized as a
whole. Conventionally, the equations for parameter estimation have to be
derived and implemented depending on the models. However, it becomes harder to
derive and implement those of a larger scale model. To solve these problems, in
this paper, we propose a method for parameter estimation by communicating the
minimal parameters between various modules while maintaining their programmatic
independence. Therefore, Serket makes it easy to construct large-scale models
and estimate their parameters via the connection of modules. Experimental
results demonstrated that the model can be constructed by connecting modules,
the parameters can be optimized as a whole, and they are comparable with the
original models that we have proposed
Double Articulation Analyzer with Prosody for Unsupervised Word and Phoneme Discovery
Infants acquire words and phonemes from unsegmented speech signals using
segmentation cues, such as distributional, prosodic, and co-occurrence cues.
Many pre-existing computational models that represent the process tend to focus
on distributional or prosodic cues. This paper proposes a nonparametric
Bayesian probabilistic generative model called the prosodic hierarchical
Dirichlet process-hidden language model (Prosodic HDP-HLM). Prosodic HDP-HLM,
an extension of HDP-HLM, considers both prosodic and distributional cues within
a single integrative generative model. We conducted three experiments on
different types of datasets, and demonstrate the validity of the proposed
method. The results show that the Prosodic DAA successfully uses prosodic cues
and outperforms a method that solely uses distributional cues. The main
contributions of this study are as follows: 1) We develop a probabilistic
generative model for time series data including prosody that potentially has a
double articulation structure; 2) We propose the Prosodic DAA by deriving the
inference procedure for Prosodic HDP-HLM and show that Prosodic DAA can
discover words directly from continuous human speech signals using statistical
information and prosodic information in an unsupervised manner; 3) We show that
prosodic cues contribute to word segmentation more in naturally distributed
case words, i.e., they follow Zipf's law.Comment: 11 pages, Submitted to IEEE Transactions on Cognitive and
Developmental System
Semantic Mapping Based on Spatial Concepts for Grounding Words Related to Places in Daily Environments
An autonomous robot performing tasks in a human environment needs to recognize semantic information about places. Semantic mapping is a task in which suitable semantic information is assigned to an environmental map so that a robot can communicate with people and appropriately perform tasks requested by its users. We propose a novel statistical semantic mapping method called SpCoMapping, which integrates probabilistic spatial concept acquisition based on multimodal sensor information and a Markov random field applied for learning the arbitrary shape of a place on a map.SpCoMapping can connect multiple words to a place in a semantic mapping process using user utterances without pre-setting the list of place names. We also develop a nonparametric Bayesian extension of SpCoMapping that can automatically estimate an adequate number of categories. In the experiment in the simulation environments, we showed that the proposed method generated better semantic maps than previous semantic mapping methods; our semantic maps have categories and shapes similar to the ground truth provided by the user. In addition, we showed that SpCoMapping could generate appropriate semantic maps in a real-world environment
Spatial Statistical Models: an overview under the Bayesian Approach
Spatial documentation is exponentially increasing given the availability of
Big IoT Data, enabled by the devices miniaturization and data storage capacity.
Bayesian spatial statistics is a useful statistical tool to determine the
dependence structure and hidden patterns over space through prior knowledge and
data likelihood. Nevertheless, this modeling class is not well explored as the
classification and regression machine learning models given their simplicity
and often weak (data) independence supposition. In this manner, this systematic
review aimed to unravel the main models presented in the literature in the past
20 years, identify gaps, and research opportunities. Elements such as random
fields, spatial domains, prior specification, covariance function, and
numerical approximations were discussed. This work explored the two subclasses
of spatial smoothing global and local.Comment: 33 pages, 6 figure