Abstract. We propose a sub-symbolic connectionist model in which a func-tionally compositional system self-organizes by learning a provided set of goal-directed actions. This approach is compatible with an idea taken from usage-based accounts of the developmental learning of language, especially one theory of infants ’ acquisition process of symbols. The presented model potentially ex-plains a possible continuous process underlying the transitions from rote knowl-edge to systematized knowledge by drawing an analogy to the formation process of a geometric regular arrangement of points. Based on the experimental results, the essential underlying process is discussed.
Population of simulated agents controlled by dynamical neural networks are trained by artificial evo...
Abstract. We suggest that the primary motivation for an agent to con-struct a symbol-meaning mapping...
We present here a hierarchical model for the evolution of compositional language. The model has the ...
Abstract—We propose a sub-symbolic connectionist model in which a compositional system self-organize...
This thesis demonstrates how the power of symbolic processing can be exploited in the learning of lo...
The ability to combine words into novel sentences has been used to argue that humans have symbolic l...
We present an experimental and simulative method for exploring the properties of symbolic action rep...
We present a novel connectionist model for acquiring the semantics of language through the behaviora...
This paper presents an agent-based model that studies the emergence and evolution of a language syst...
In the recent decades, alternative notions regarding the role of symbols in intelligence in natural ...
This paper presents an agent-based model that studies the emergence and evolution of a language syst...
This chapter gives an overview of different experiments that have been performed to demonstrate how ...
The Symbolic Grounding Problem is viewed as a by-product of the classical cognitivist approach to st...
In this article, we study the emergence of associations between words and concepts using the self-or...
This paper proposes work that applies insights from meaning representation systems for in-depth natu...
Population of simulated agents controlled by dynamical neural networks are trained by artificial evo...
Abstract. We suggest that the primary motivation for an agent to con-struct a symbol-meaning mapping...
We present here a hierarchical model for the evolution of compositional language. The model has the ...
Abstract—We propose a sub-symbolic connectionist model in which a compositional system self-organize...
This thesis demonstrates how the power of symbolic processing can be exploited in the learning of lo...
The ability to combine words into novel sentences has been used to argue that humans have symbolic l...
We present an experimental and simulative method for exploring the properties of symbolic action rep...
We present a novel connectionist model for acquiring the semantics of language through the behaviora...
This paper presents an agent-based model that studies the emergence and evolution of a language syst...
In the recent decades, alternative notions regarding the role of symbols in intelligence in natural ...
This paper presents an agent-based model that studies the emergence and evolution of a language syst...
This chapter gives an overview of different experiments that have been performed to demonstrate how ...
The Symbolic Grounding Problem is viewed as a by-product of the classical cognitivist approach to st...
In this article, we study the emergence of associations between words and concepts using the self-or...
This paper proposes work that applies insights from meaning representation systems for in-depth natu...
Population of simulated agents controlled by dynamical neural networks are trained by artificial evo...
Abstract. We suggest that the primary motivation for an agent to con-struct a symbol-meaning mapping...
We present here a hierarchical model for the evolution of compositional language. The model has the ...