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III.3 Scope of Quantum Artificial Life Regarding the emergence of complexity, the route towards the scalability of our quantum algorithm is intrinsically related to the inclusion of more degrees of freedom in the description of quantum living units. These may be introduced by simply increasing the number of qubits, and... |
A different question is the scalability of the current model without including any modification. Notice that, in our protocol, the information about the lifetime and the predator-prey character is classical and encoded in the mean value [MATH] of the phenotype and the genotype of the individual, respectively. Therefore... |
This experimental realization of the proposed quantum algorithm represents the consolidation of the theoretical framework of quantum artificial life. The improvement in scalable quantum computers will soon allow us for more accurate quantum emulations with growing complexity towards quantum supremacy, even considering ... |
All in all, the experiments presented here entail the validation of quantum artificial life in the lab and, in particular, in cloud quantum computers as that of IBM. Still another interesting step would be the development of autonomous quantum devices following the theoretical and experimental results in quantum cellul... |
Acknowledgements We thank Armando Pérez-Leija and Alexander Szameit for enthusiastic and enlightening discussions. We acknowledge the use of IBM Quantum Experience for this work. The views expressed are those of the authors and do not reflect the official policy or position of IBM or the IBM Quantum Experience team. We... |
Author Contributions U. A.-R. performed the experiments on the IBM Quantum Experience. U. A.-R., M. S., L. L., and E. S. developed the model and analyzed the data. All authors contributed to writing the manuscript. Additional Information Competing interests: The authors declare no competing interests. |
# Source: arxiv 1801.02086 # Title: Complexity, Development, and Evolution in Morphogenetic Collective Systems # Sections: all # Downloaded: 2026-03-03T01:59:36.748392+00:00 |
Complexity, Development, and Evolution in Morphogenetic Collective Systems Abstract Many living and non-living complex systems can be modeled and understood as collective systems made of heterogeneous components that self-organize and generate nontrivial morphological structures and behaviors. This chapter presents a b... |
Introduction Various living and non-living systems are collective systems in the sense that they consist of a large number of smaller components. Those microscopic components interact with each other to show a wide variety of self-organizing macroscopic structures and behaviors, which have been subject to many scientif... |
Typical assumptions often made in earlier mathematical/computational models of self-organizing collectives include the homogeneity of individual components’ properties and behavioral rules within a collective. Such homogeneity assumptions have merit in simplifying models and allowing for analytical prediction of the mo... |
, termite colony building and maintenance , and growth and self-organization of human social systems . Those real-world complex collectives consist of heterogeneous components whose behavioral types can change dynamically via active information exchange among locally connected neighbors. These properties of components ... |
In this chapter, we present a brief summary of our recent effort in investigating several aspects of complex morphogenetic collective systems that involve (1) heterogeneous components, (2) dynamic differentiation/re-differentiation of the components, and (3) local information sharing among the components. Our objective... |
The rest of this chapter is structured roughly following the topics of this proceedings volume— evolution, development, and complexity —though we will discuss them in a reversed order. We will first propose a classification scheme of several distinct complexity levels of morphogenetic collective systems based on their ... |
Functional Complexity Levels of Morphogenetic Collective Systems Our first task is to identify what kind of properties are typically seen in real-world complex collective systems but often omitted for simplicity in the literature on mathematical/computational models of those systems. In |
, we selected the following three as the key properties essential for self-organization of morphogenetic collective systems yet often ignored in the literature: |
1. Heterogeneity of components 2. Differentiation/re-differentiation of components 3. Local information sharing among components |
Heterogeneity of components means that there are multiple, distinct types of components whose behaviors are different from each other. Note that these types are not necessarily a simple rewording of dynamical states. Instead, each type may have multiple dynamical states within itself, while its behavioral rules as a wh... |
Mathematically speaking, distinguishing presence/absence of each of these three properties would define a total of [MATH] possible classes of collective systems. However, we claim that there are some hierarchical relationships among those three properties. Specifically, differentiation/re-differentiation of components ... |
(Fig. ): Class A Homogeneous collective Class B Heterogeneous collective Class C Heterogeneous collective with dynamic (re-)differentiation |
Class D Heterogeneous collective with dynamic (re-)differentiation and local information sharing The dynamics of components in each of these four classes can be represented mathematically as follows |
Class A [MATH] Class B [MATH] Class C [MATH] [MATH] Class D [MATH] [MATH] [MATH] [MATH] Here [MATH] [MATH] , and [MATH] are individual component [MATH] ’s behavior, observation, and type at time [MATH] , respectively ( [MATH] is a time-invariant type of component [MATH] ); [MATH] and [MATH] are model functions; and [MA... |
[MATH] . These mathematical formulations help clarify the hierarchical relationships among the four complexity levels. Following these formulations, we will construct a specific computational model of morphogenetic collective systems to facilitate systematic investigation of the proposed four complexity levels and thei... |
Developmental Models: Morphogenetic Swarm Chemistry We utilized our earlier “Swarm Chemistry” model to construct a new computational model of morphogenetic collective systems. Swarm Chemistry is a revised version of Reynolds’ well-known self-propelled particle swarm model known as “Boids” |
. In Swarm Chemistry, multiple types of components with different kinetic behavioral parameters are mixed together. Their behavioral parameters are represented in a “recipe” as shown in Fig. . Therefore, the Swarm Chemistry model is already capable of representing both Class A (homogeneous) and Class B (heterogeneous) ... |
To make individual components capable of dynamic differentiation/re-differentiation and local information sharing, we made several extensions to Swarm Chemistry |
. First, we made each individual component able to obtain information about its own dynamical type and its local environment in the form of observation vector |
[MATH] (Fig. ), and then utilize this vector to decide which dynamical type it should assume. This allows for dynamic (re-)differentiation required for Class C/D collective systems. This decision making process was implemented via multiplication of preference weight matrix |
[MATH] to the observation vector [MATH] , so that letting [MATH] represents Class A/B systems as well. The second model extension was to introduce local information sharing coefficient |
[MATH] , with which the actual input vector multiplied by [MATH] was calculated as the weighted average between the component’s own observation vector and the local average of all the observation vectors of neighbor components. Changing the value of [MATH] represents switching between Class C and Class D collective sys... |
. This expanded model is called “Morphogenetic Swarm Chemistry” hereafter. More details can be found in Differences of Developmental Processes Across Complexity Levels |
We conducted a series of computational experiments using the Morphogenetic Swarm Chemistry model to investigate the differences of their developmental processes across the four complexity levels. This was conducted by detecting statistical differences in topologies and behaviors of self-organizing patterns that were co... |
(number of connected components, average size of connected components, homogeneity of sizes of connected components, size of largest connected component, average size of non-largest connected components, average clustering coefficient, link density) that were measured on a network reconstructed from the individual comp... |
. These new metrics allowed us to capture topological properties of the collectives that would not have been captured by using simple kinetic metrics only. |
Results showed significant differences in most of the metrics between the four different classes of morphogenetic collective systems |
. Specifically, heterogeneity of components had a strong impact on the system’s structure and behavior, and dynamic differentiation/re-differentiation of components and local information sharing helped the system maintain spatially adjacent, coherent organization. Statistical differences were particularly significant f... |
As described above, straightforward statistical analysis placed the properties of Class C/D systems somewhere in between Class A and Class B, while it did not clarify whether Class C/D systems had any truly unique properties different from Classes A or B. Therefore, we conducted more in-depth, meta-level comparative an... |
. Behavioral diversities were measured for each class by computing the approximated volume of behavior space coverage, the average pairwise distance of two randomly selected behaviors in the behavioral space, and the differential entropy |
of the smoothed behavior distribution. More details can be found in . Results indicated that the dynamic (re-)differentiation of individual components, which was unique to Class C/D systems, played a crucial role in increasing the diversity in possible behaviors of collective systems (Fig. ). This new finding revealed ... |
Evolutionary Design of Morphogenetic Collective Systems The remaining question we want to address is how to design novel self-organizing patterns of morphogenetic collective systems. Unlike conventional engineered systems for which clear design principles and methodologies exist, complex systems show nontrivial emergen... |
. To design such systems, the evolutionary approach has been demonstrated to be one of the most effective means . Here we adopt two different evolutionary approaches: one is interactive evolutionary computation (IEC) |
and the other is spontaneous evolution within a simulated artificial ecosystem In the IEC approach, we developed a novel IEC framework called “Hyper-Interactive Evolutionary Computation (HIEC)” |
, in which human users act not only as a fitness evaluator but also as an active initiator of evolutionary changes. HIEC was found to be highly effective in exploring the extremely high dimensional design space of Swarm Chemistry, discovering a number of nontrivial, life-like morphological patterns and dynamic behavior... |
(Fig. ), which is highly unique given that behaviors of complex systems generally depend heavily on spatial dimensions in which they develop. |
Finally, in the spontaneous evolution approach, we replaced the human users in IEC with microscopic “physics laws” that would govern transmission of recipe information among individual components (as evolutionary operators acting at local scales) and macroscopic measurements of “interestingness” (as assessments of evol... |
. Specifically, recipe information was assumed to be transmitted between two colliding particles (with stochastic mutations possible at a small probability). The direction of transmission was determined by specific microscopic laws. These laws were perturbed globally at certain intervals to introduce variations and thu... |
. This spontaneous evolution approach was shown to be very powerful in continuously producing nontrivial morphologies. An example is given in Fig. , and other illustrative evolutionary processes can be found online |
In the meantime, it was also noticed that evolutionary exploration was much less active in three-dimensional space than in two-dimensional one |
, despite the robustness of self-organization against the same dimensional changes. This sensitivity was considered to be due to the fact that spontaneous evolution heavily relies on collisions between particles, which would become fundamentally less frequent in 3D space |
Conclusions In this chapter, we gave a condensed summary of our recent project that explored the complexity, development, and evolution of morphogenetic collective systems. The classification scheme of morphogenetic collective systems we proposed was among the first that focuses on functional and interactive capabiliti... |
The numerical simulation results obtained by using Morphogenetic Swarm Chemistry demonstrated that each of the characteristic properties of collective systems has unique, distinct effects on the resulting morphogenetic processes. Heterogeneity of components has quite significant effects on various properties of the col... |
This short chapter is obviously not sufficient to cover the whole scope of the project, which also produced several more application-oriented contributions that were not discussed here. Interested readers are encouraged to visit our project website |
Acknowledgments This material is based upon work supported by the National Science Foundation under Grant No. 1319152. The author thanks Benjamin James Bush, Shelley Dionne, Craig Laramee, David Sloan Wilson, and Chun Wong for their contributions to this project. |
# Source: arxiv 1801.08829 # Title: Symbol Emergence in Cognitive Developmental Systems: a Survey # Sections: all # Downloaded: 2026-03-03T02:00:45.515605+00:00 |
Symbol Emergence in Cognitive Developmental Systems: a Survey Abstract Humans use signs, e.g., sentences in a spoken language, for communication and thought. Hence, symbol systems like language are crucial for our communication with other agents and adaptation to our real-world environment. The symbol systems we use in... |
Index Terms: Symbol emergence, developmental robotics, artificial intelligence, symbol grounding, language acquisition INTRODUCTION |
SYMBOLS, such as language, are used externally and internally when we communicate with others, including cognitive robots, or think about something. Gaining the ability to use symbol systems, which include not only natural language but also gestures, traffic signs, and other culturally or habitually determined signs, i... |
Figure depicts a human–robot interaction scenario. Here, a robot is handing over a cup following the utterance of the command ``give me the cup" by the person. A string of letters (written or spoken) has to be translated into a series of motor actions by the robot, with reference to the context and the environment. Thi... |
In addition to learning or internalizing the pre-existing meaning and usage of signs, we humans can also invent and generate signs to represent things in our daily life. This immediately suggests that symbol systems are not static systems in our society, but rather dynamic systems, owing to the semiotic adaptiveness an... |
With respect to the term ``symbol'', many scholars still have significant confusion about its meaning. They tend to confuse symbols in symbolic AI with symbols in human society. When we talk about symbol emergence, we refer to the latter case. Note that this paper is not about symbolic AI, but rather focuses on the que... |
The symbol grounding problem was proposed by Harnad . Their paper started with symbolic AI. Symbolic AI is a design philosophy of AI based on the bold physical symbol system hypothesis proposed by Newell |
(see Section IV-A ), which has already been rejected practically, at least in the context of creating cognitive systems in the real-world environment. The term ``symbol'' in symbolic AI is historically rooted in symbolic/mathematical logic. It is originally different from the symbols in our daily life. The motivation o... |
First, it is therein assumed that symbols are physical tokens and considered that symbols in communication (e.g., words), and tokens in one’s mind, (i.e., internal representations) are the same. This comes from the physical symbol system hypothesis, which we now need to say is simply wrong. It is fair to assume that ou... |
Meanwhile, we still have challenges in making artificial cognitive systems that can learn and understand the meaning of symbols, e.g., language. However, most studies, tackling the challenges in developing cognitive systems that can learn language and adaptive behaviors in the real-world environment, rarely use symboli... |
Thus, the symbol emergence problem is indeed a multifaceted problem that should take different sources of information for learning into account. The required computational models should enable the simultaneous learning of action primitives together with the syntax of object manipulations, complemented by lexicographic,... |
Thus, in this paper, we address the interdisciplinary problem history of symbols and symbol emergence in cognitive developmental systems discussed in different fields and also point out their mutual relations . We describe recent work and approaches to solve this problem, and provide a discussion of the definitions and... |
, as well as an integrative viewpoint on symbol emergence describing potential future challenges. Section II introduces the aspect of semiotics. Sections III and IV describe the problem history in related fields concerning biological systems (i.e., mainly humans) and artificial systems, respectively. Section provides a... |
II Semiotics: from signs to symbols Symbol is not only a term in AI and cognitive robotics, but is also frequently used in our human society. The nature of the symbols that humans use in their daily life is studied in semiotics , which is the study of signs that mediate the process of meaning formation and allow for me... |
Endowing robots with the capability to understand and deal with symbols in human society (e.g., Fig. ) requires a robot to learn to use symbol systems that we humans use in our society. Symbols are formed, updated, and modulated by humans to communicate and collaborate with each other in our daily life. Therefore, any ... |
To achieve this, we can adopt the definition of symbol given by Peircean semiotics and represented by a semiotic triad (Fig. ). Peircean semiotics considers a symbol as a process called semiosis . Semiosis has three elements, i.e., sign (representamen) object , and interpretant . The triadic relationship is illustrated... |
Naively, people tend to think there is a fixed relationship between a sign and its object. In our natural communication and social life, the meaning of signs hugely depends on contexts, participants, communities, cultures, and languages. It is the third term—interpretant—which gives our semiotic communication a very hi... |
. Thus, the meaning that a sign conveys to a person can change depending on its interpretant. In this paper, we employ this definition of symbol because our target is semiotic communication not symbolic AI. Note that signs do not have to be speech signals or written letters. Any type of stimulus, i.e. any signal, could... |
By contrast, Saussurian semiotics places more emphasis on a systematic perspective of symbols as opposed to the individual dynamics of a symbol. This model assumes that symbol consists of sign (signifier) and referent (signified) leading to a dyadic structure. This view is similar to the view of symbolic AI. In symboli... |
Common to both—Peircean and Saussurian semiotics—is the fact that sign and object are heavily intertwined. A sign that is not linked to objects or events directly or indirectly is essentially meaningless |
. Only when we perceive the sign of a symbol, can we infer which object the sign represents. Furthermore, it should be noted that a symbol generates a boundary between the ``signified'' (object) and anything else. Therefore, symbol systems introduce discrete structures into the world. |
The supporters of both models also insist that the relationship between sign and referent (object) is arbitrary. Arbitrariness does not indicate randomness or disorder. Rather, it means that symbol systems defining relationships between signs and referents can be different from each other, depending on languages, cultu... |
Scholars who may be familiar with symbolic AI may argue that the advantage of symbolic AI is that one can manipulate symbols independently of the substrate on which they are grounded. Arguably, this has been regarded as the central, major strength that makes symbol systems so useful. As remarked by Harnad |
, a symbol is a part of a symbol system, i.e., the notion of a symbol in isolation is not a useful one. The question about symbol manipulation has also led to a vigorous debate between the Cartesian view (mind without body) of symbolic AI as compared to the view presented by embodied cognition , which posits that the m... |
Numerous studies in semiotics, including cultural studies, provided a deeper understanding of symbol systems. However, so far there is no computational model that can reproduce the dynamics of semiosis in the real world. In addition, most of the studies in semiotics focused on existing symbol systems and rarely conside... |
III Problem history 1: Symbol emergence in biological systems III-A Evolutionary viewpoint: from actions to symbols From an evolutionary viewpoint, symbol emergence should also be explained in the context of adaptation to the environment. |
With few exceptions, and different from plants, all animals are able to move. Considering the phylogeny of the animal kingdom, movement patterns, actions, and action sequences have become more and more complex. In parallel to this development, more complex cognitive traits have emerged over 2–3 billion years from proto... |
. For example, Aloimonos et al. and Lee et al. have developed a context-free action grammar to describe human manipulations We would now argue that action and action understanding come before symbolization, and that the grammatical structure implicit to action lends itself as a natural scaffold to the latter. |
The vast majority of all animals perform their movements almost exclusively in a reactive, reflex-like manner, responding to stimuli from the environment. This leads to a repertoire of various movement patterns, which we could consider as the basic syntactic elements in a grammar of actions. Chaining such actions leads... |
The making-explicit of action semantics does not come easily, and action understanding is not a one-shot process that suddenly appeared in evolution. Potentially, all those animals that can purposefully ``make things happen'' achieve first steps along this path. In other words, a creature that is able to prepare the gr... |
) may be less relevant to our practical stance: if I can purposefully make it happen, I have understood it. Repetitions of the same or a similar action in different situations, and hence of different action variants in an action class, can make it possible to extract action pre- as well as post-conditions, the cause an... |
Symbols are at the far end of a whole chain of complex processes, and many animals can perform earlier steps in this chain but not later ones, making them ``less cognitive'' than humans. What do we have in mind here? Figure depicts a possible pyramid that leads from simple, clearly noncognitive processes step by step u... |
Whereas action may have been the origin for triggering concept formation, by now we—humans—have surpassed this stage and have taken concept formation into different and sometimes highly abstract domains. |
We would argue that the formation of such discrete concepts is central to the emergence of symbols and that the level of concepts is the one where symbolization can set in. The term Object in Fig. indeed takes the wider meaning of concept of object (-class) |
In particular, one can now see that attaching a sign to a concept is simple. You could use anything. Is this all and are we done now? |
We believe the remaining difficulty lies in the communication process. Symbols (based on signs) are initially only meaningful in a communication process. This may have started as one-step memos of an individual, such as the drawing of a wiggly line on the ground to remind your stone-age self to go to the river as sugge... |
. This may have led to more complex silent discourse with yourself and in parallel to true inter-individual communication. It is indeed totally trivial to attach a sign, say a string of letters, to a concept (once the latter exists), but to understand my uttering of this string, my listener and I need to share the same... |
Now, it also becomes clearer why humans and some nonhuman primates can handle symbols : These genera are highly social and continuously engage in inter-individual exchanges; they communicate. Wolves do this, too. Why did they not begin to develop symbolic thinking? We believe this is where the grammatical aspect of act... |
While suggestive, it necessarily remains unclear whether or not evolution indeed followed this path. We are here, however, centrally concerned not with this but rather with artificial cognitive developmental systems, asking how they could learn to use symbols. The above discussion posits that this could possibly be bes... |
III-B Neuroscientific viewpoint: from neural representations to symbols Humans use symbols. On the one hand, it is known that all the representations in the brain are highly dynamic and distributed, and thus only a snapshot of the brain's activity and state could come close to what we may call a symbol in symbolic AI. ... |
This view critically assumes that concept-like internal representations in the brain must have emerged before expressed symbols for social communication. The initial evolutionary pressure for their formation was not social communication but rather for internal computation and communication in the brain. |
There are intriguing examples of such localized neural representations that may play the role of a concept; however, whether these symbol- or concept-representations are manipulated for inference and planning is largely unknown. |
A good candidate for the representation of action concepts is the neural response exhibited by mirror neurons . These neurons were initially discovered in the ventral premotor cortex of macaque monkeys (area F5), and evidence exists that humans are also endowed with a mirror system |
. Macaque mirror neurons become activated when a monkey executes a grasp action, as well as when the monkey observes another monkey or human perform a similar action |
. This duality lends support to the plausibility of the idea that a mirror neuron activity represents an abstraction of an action for further processing, as opposed to being an intermediate representation of action production. If the former is indeed correct, then mirror neurons should be considered as encoding concept... |
In general, the minute details of an action are not critical for most of the mirror neurons ; therefore, their activity is probably not a precise motor code. Initial reports on mirror neurons focused on their role for action understanding; but later, higher-level functions have also been attributed to them, such as und... |
It would be very valuable to decipher how mirror neuron activity is used elsewhere in the brain. If it were used as the basis for planning, we could happily claim that some sort of concept representation is undertaken by these neurons. In this case, we may feel comfortable calling the mirror activity a neural symbol of... |
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