Emergence of Protosentences in Artificial Communicating Systems

Uno, Ryoko; Marocco, Davide; Nolfi, Stefano; Ikegami, Takashi

doi:10.1109/tamd.2011.2120608

Cited by 38 publications

(13 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This result is in line with other research in semiotics that shows the development of spontaneous communicative conventions, such as the self-organisation of turn-taking and dialogical communication forms, among different media from spoken languages to graphical communication systems [16]. Moreover, note that such a self-organised form of communicative interaction has been observed in many computational models in which artificial agents were freely allowed to self-organise their communication to complete collective tasks [7][8][9]17].…”

Section: A Emergence Of Communication Systemsupporting

confidence: 90%

“…However, from the social perspective, the majority of the work is based on disembodied and non-dynamic models that fail to properly account for dynamical embodied interactions between individuals [5,6]. Only recently some scholars have started to investigate social interaction from an embodied and dynamic perspective, both with generative and embodied artificial models [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Co-creativity of communication system on behavioral interaction

Iizuka

Ando

Maeda

et al. 2012

2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication

Self Cite

View full text Add to dashboard Cite

This study presents an experiment that integrates a semiotic investigation with a dynamical perspective on embodied social interactions. The primary objective is to study co-creativity of a communication system on the human-human nonverbal interaction. Throughout the experiment, the results of established communication system on uni-and bi-directional interactions are compared. Finally, the role of co-creativity in the evolution of communication systems will be discussed.

show abstract

Section: A Emergence Of Communication Systemsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Co-creativity of communication system on behavioral interaction

Iizuka

Ando

Maeda

et al. 2012

2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication

Self Cite

View full text Add to dashboard Cite

show abstract

“…Now, we evaluate our communicative behavior quantitatively in terms of these joint attentions. We use mutuality as a measure of participatory joint attention, as it is regarded as a bidirectional interaction in [21,22]. For instrumental joint attention, we measure the information flow to show the actual information transfer due to communication.…”

Section: Quantitative Analysis Of Evolved Behaviorsmentioning

confidence: 99%

Evolution of communication through differentiation of communicative and goal-directed behaviors

Shibuya

Iizuka

Yamamoto

2017

Artif Life Robotics

View full text Add to dashboard Cite

Many studies have focused on the evolution of communication in artificial life, often presupposing dedicated communication channels such as voices or pheromones. However, it is plausible that only behavior existed when communication began and that some behaviors gradually evolved as a mean of communication. A study by Quinn has shown that communication can evolve purely through the behavior for two agents in a simulated environment. We extend this study to a more complex communication using three agents. In the simulation, the agents are equipped with two wheels and eight IR sensors and are controlled by identical neural networks. This network evolved by applying a genetic algorithm to a single population. After evolution, the agents' behaviors had differentiated into communicative and goal-directed types. We analyze the differences between these two types of behavior quantitatively, and show that the evolved behaviors have communication properties such as mutuality, information flow, and invariance of the motion gain.

show abstract

“…The emergence of linguistic structure is outside the scope of this work. For studies of grammar, syntax, and generational learning, see (Steels, 2000;Kirby, 2002;Cangelosi et al, 2010;Uno et al, 2011).…”

Section: Symbol Grounding Versus Languagementioning

confidence: 99%

“…Harnad (1990) highlighted the challenging nature of the relationship between a word and its meaning, calling it the symbol grounding problem. Words for some physical objects can be grounded in sensorimotor percepts (Vogt, 2002;Steels, 2005;Roy, 2005;Steels, 2008;Schulz et al, 2011aSchulz et al, , 2011b, while other concepts may be grounded in secondary structures (Cangelosi and Riga, 2006;Cangelosi et al, 2010;Schulz et al, 2011aSchulz et al, , 2011bUno et al, 2011).…”

Section: Introduction: Beyond Clock Timementioning

confidence: 99%

Evolving spatial and temporal lexicons across different cognitive architectures

Heath¹

View full text Add to dashboard Cite

Communication between mobile robots requires a transfer of symbols, where each symbol signifies a meaning. However, in typical applications, meaning has been ascribed to the symbols by the engineers that have programmed the robots. This thesis explores an alternative: the use of algorithms and representations that allow mobile robots to evolve a shared set of symbols where the meanings of the symbols are derived from the robots' sensors and cognition.Mobile robots have two important properties that affect the learning of symbols, i) that they are capable of locomotion through space over time; and ii) that they come in many different configurations with different architectures. Previous work has demonstrated that mobile robots can learn shared lexicons to describe space through perceptual referents and referents grounded in cognitive maps. However, open questions remain as to how mobile robots can learn to communicate using temporal terms, and how learning lexicons is affected by different cognitive architectures.The major research question addressed in this thesis is how can mobile robots develop spatial and temporal lexicons across different cognitive architectures? Three facets of language learning are considered particularly important for robots with different cognitive architectures: i) the ability to ground terms in cognition;ii) the ability to ground identical terms in different sensors and cognition for each robot; and iii) the ability to handle referential uncertainty -the difficulty of linking words to meanings within ambiguous contexts.Pairs of mobile robots are used to develop lexicons for spatial and temporal terms and to study each of these abilities. The terms developed by the robots are tested by organizing spatial and temporal tasks and extended to additional terms through grounding transfer.In this thesis, language learning is studied within a framework defined by Peirce's semiotic triangle and building on previous Lingodroid studies. Conversations between robots are used to socially ground symbols within the robots' spatial and temporal cognition. Distributed lexicon tables are used to store links between words and meanings. As the lexicons evolve the words are analyzed for immediate usability, and the final lexicons are analyzed for coherence.Four studies to analyze different aspects of lexicon learning were completed. Study I addressed the aims of learning duration terms using mobile robots and using grounded spatial and temporal language together to perform joint tasks. Identical mobile robots were used to ground terms for time in durations using clocks (time since the last meeting). The robots were able to develop coherent lexicons, and successfully organize future meetings using learned terms.Study II addressed the aim of learning event-based temporal terms using mobile robots. Identical mobile robots were used to ground terms for time in sunlight levels (time of day). The robots required the ability to ground terms in features formed from a brightness level and its derivative. Aga...

show abstract

Emergence of Protosentences in Artificial Communicating Systems

Cited by 38 publications

References 22 publications

Co-creativity of communication system on behavioral interaction

Co-creativity of communication system on behavioral interaction

Evolution of communication through differentiation of communicative and goal-directed behaviors

Evolving spatial and temporal lexicons across different cognitive architectures

Contact Info

Product

Resources

About