Scholarly article on topic 'Zer0: An Emergent and Autopoietic Multi-agent System for Novelty Creation in Game Art through Gesture Interaction'

Zer0: An Emergent and Autopoietic Multi-agent System for Novelty Creation in Game Art through Gesture Interaction Academic research paper on "Computer and information sciences"

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Abstract of research paper on Computer and information sciences, author of scientific article — Francisco de Paula Barretto, Suzete Venturelli

Abstract This paper describes a transdisciplinary theoretical-practical research, which address on the discussion about the possible applications of Multi-agent Systems, underlying the Maturana and Varela's autopoietic concept considering the achievement of emergent results as heuristics to creativity. Autopoiesis (from the Greek “auto” which means “itself” and “poiesis” which means “creation”) describes the autonomous systems, able to self-reproduce and self-regulate, while iterating with the environment. In order to explore those concepts, we present Zer0, a game that invites the player to drift in a universe ruled by geometric shapes. Through interactions with other shapes, the player is able to evolve from a single line shape to more complex ones. Zer0 is a multi-agent system able to compose emergent music in real time. As interactions occur, chain reactions create the game soundtrack. There are two main agents involved: the player and the other shapes. While the player enjoys the ride, the other shapes are trying to interact with each other in order to expand their lifespan. The communication between agents is made through generated pulses, which are emitted by them and also serves as sonar, in order to perceive the environment.

Academic research paper on topic "Zer0: An Emergent and Autopoietic Multi-agent System for Novelty Creation in Game Art through Gesture Interaction"

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Procedia Manufacturing 3 (2015) 850 - 857

6th International Conference on Applied Human Factors and Ergonomics (AHFE 2015) and the

Affiliated Conferences, AHFE 2015

Zer0: an emergent and autopoietic multi-agent system for novelty creation in game art through gesture interaction

Francisco de Paula Barretto*, Suzete Venturelli

Computer Art Research Lab, University of Brasilia, Brasilia, Federal District, Brazil

Abstract

This paper describes a transdisciplinary theoretical-practical research, which address on the discussion about the possible applications of Multi-agent Systems, underlying the Maturana and Varela's autopoietic concept considering the achievement of emergent results as heuristics to creativity. Autopoiesis (from the Greek "auto" which means "itself' and "poiesis" which means "creation") describes the autonomous systems, able to self-reproduce and self-regulate, while iterating with the environment. In order to explore those concepts, we present Zer0, a game that invites the player to drift in a universe ruled by geometric shapes. Through interactions with other shapes, the player is able to evolve from a single line shape to more complex ones. Zer0 is a multi-agent system able to compose emergent music in real time. As interactions occur, chain reactions create the game soundtrack. There are two main agents involved: the player and the other shapes. While the player enjoys the ride, the other shapes are trying to interact with each other in order to expand their lifespan. The communication between agents is made through generated pulses, which are emitted by them and also serves as sonar, in order to perceive the environment.

© 2015 The Authors.PublishedbyElsevierB.V. This is an open access article under the CC BY-NC-ND license

(http://creativecommons.Org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of AHFE Conference

Keywords:Multi-agent systems; Creativity; Emergence; Autopoiesis; Artificial intelligence; Interaction

* Corresponding author. Tel.: +55-61-81894589. E-mail address: kikobarretto@gmail.com

2351-9789 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license

(http://creativecommons.Org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of AHFE Conference

doi: 10.1016/j.promfg.2015.07.341

1. Introduction

The Artificial Intelligent researchers aim, through the improvement of specific models and techniques, to achieve the best solutions for specific problems such as machine learning, computer vision and computer creativity. The discussion we intend to bring on with this paper is about the possible applications of AI that underlie the emergence and autopoietic concepts. The first concept is defined by Peter Cariani [1] as the emergence of new entities that in one sense or another, could not have been predicted based on what preceded them, while autopoiesis (from the Greek "auto'" which means "itself' and "poiesis" which means "creation") describes the autonomous systems, able to self-reproduce and self-regulate, while iterate with the environment. This environment iteration might unroll, only in an indirect way, changes on the autopoietic system's internal processes and structures [2] that might lead to a deterministic-emergent transition.

According to Stephen Wilson [3], the development of algorithms and heuristics that allow computers to perform complex and sophisticated analysis or demonstrate complex behavior, as create artworks, represents some of the greatest challenges of modern scientific research. This challenge derives not only from the development of new technologies capable of support the computational requirements of such algorithms, but also the need to understand the phenomenon of intelligence through new perspectives and approaches able to raise new questions on this philosophical issue. Silvia Laurentiz [4] points out that one of the main questions raised by the AI field is the exact definition of the vocabulary used, as what we mean by "intelligence" for example.

We do not intend to engage in such a subjective discussion but rather present another perspective that might help understand the intelligent phenomena based on cognitive AI. In this approach, the system's intelligent behavior, as described by Brian Smith [5], requires knowledge representation and machine learning. Therefore, we might ask how to design Smith's knowledge representation in an autopoietic system and how emergence can be seen as creativity.

Finally this paper shortly inquires on the relevance of considering the autopoietic and emergence principles while designing a multi-agent system: his knowledge representation and main capabilities to sense the world and co-evolve with it, considering previous works [6] [7].

2. Emergence

The emergence concept is defined according to Peter Cariani [1] as the development of new entities that, in one sense or another, could not have been predicted based on what preceded them. The word itself has roots on Latin "emergere" that means "bring to light". One can also understand emergence as the appearance of macro patterns due to microprocesses.

We can find in nature several examples of emergence. According to Peter Cariani, the main emergent events of the universe includes particles, atoms and molecules creation, in a microscale, and stars, galaxies and black holes formations in a macroscale. One may even question if the laws of physics and even time itself are emergent aspects from the evolution of the universe.

However, emergence is something broader than the mere appearance of new structures and new patterns. It also includes fundamentally new organizations of matter and information processes along with a new world cognitive point of view. In a natural context it is clear that the emergent transitions may involve one or more of these fundamentally new formations but it does not ordinarily apply to computer models given the different context and environment in which relationships are built: cyberspace. In a binary context the establishment of new connections and the creation of new entities demand a new approach on the subject because one might question if the emergent transitions are possible in a virtual environment, which is a deterministic system.

Kujawski [8] affirms that it is possible for something new, unpredictable; emerge from a Turing machine once we understand the difference between rules and laws. The first is a set of well-defined formal procedures wile the latter represents universal conditions. There are algorithms or a set of rules behind any emergent phenomenon, regardless their nature. A good example of emergence in a simple rules system is the Game of Life, created by John Conway in the 1950s and described in [9].

COGNITIVE STRUCTURE

CO-EMERGENCE

AUTOPOIETIC SYSTEM

ENVIRONMENT

Fig. 1. Graphic representation of an autopoietic system cognitive co-emergence, simplified from [11].

3. Autopoiesis

The concept of autopoiesis, as the organization of the living, originated in the work of Chilean biologists Humberto Maturana and Francisco Varela in the 1970s [2]. This idea was developed in the context of theoretical biology and was early associated with the artificial life simulation long before the term "artificial life" have been introduced in the late 1980s in [10].

Today the concept of autopoiesis continues to have a significant impact in the field of artificial life computing. Pier Luisi presents a good review in [11]. Furthermore, there was also an effort to integrate the notion of autopoiesis to the field of cognitive sciences.

To be more precise, an autopoietic system is organized as a production processes network of components (synthesis and destruction) which: (i) continuously regenerate themselves in order to form a network able to reproduce components and (ii) this network constitutes the system as a distinct unit in the domain in which it exists. In addition to these two explicit criteria for autopoiesis, we can add another important point: that identity self-constitution implies on the creation of a relational domain between the system and its environment. Froese and Ziemke describe this relational domain in [12]. This emergent domain is not predetermined but possibly co-determined by the system and environment's organization, Figure 1. Any system that meets the criteria for autopoiesis also generates its own domain of interactions while its identity emerges.

A single cell organism, Figure 2, is a perfect example of a paradigmatic autopoietic system and illustrates the circular production network that is inherent to the autopoietic self-production system. In the unicellular case, this circular relationship is expressed by the co-dependence between the limits determined by the membrane (external) and the metabolic network (internal). This metabolic network builds itself and distinguishes from the environment as a unified system. This bounded system formation is only possible due to the external system (membrane), which prevents components from dispersing in the environment. On the other hand, this external system is only constituted because there is an internal functional metabolic network. This whole system might be artificially reproduced by AI techniques such as ANN and GA.

produce

INPUTS

COMPONENTS

METABOLIC NETWORK

Fig. 2. Single cell organism self-regulation cycle, adapted from [11].

The concept of self-organization can be interpreted in many different ways, but in terms of autopoietic is worthy of being presented by two aspects: (i) determining local-to-global, so that the process has its emerging identity global constituted and constrained as a result of local interactions and (ii) determining global-to-local and global identity where its ongoing contextual interaction constrain local interactions [13].

Finally, autopoietic systems are also autonomous systems since they are characterized by such a dynamic co-emergence but are specified within a specific domain. It is important for the creativity of a system that it's changes and adaptations of the internal mechanisms are not performed directly by an external agent, but through an internal self-regulation mechanism.

4. Artificial intelligence, autopoiesis and emergence

There is some effort within the AI field, especially in the cognitive AI area in order to turn the agent design principles more explicit. The discussion about these principles initially proposed by Rolf Pfeifer in the 1990s has been addressed in [14], [15], [16] and [13], culminating on a thorough review by Froese and Ziemke in [12].

The emergence design principle, as defined by Pfeifer, Iida and Bongard [16], is extremely relevant in this research because it demonstrates the convergence of the discussed theories towards the application of emergence as heuristics for the development of intelligent systems that demonstrate "natural" behavior. This principle is shared by many AI computational approaches in the minimal sense that the agent behavior must always emerge from the interactions with its environment.

This principle states that if we intend to develop adaptive systems, we must aim for emergence. The term emergence itself is somewhat controversial but here we use it in a pragmatic sense: something not planned or predictable. By aiming to develop an emergent agent, its cognitive structure will be the result of the history of its interactions with the environment.

To Pfeifer and Gomez [15], the relationship between behavior and emergence goes far beyond simple interactions between agent and environment. Thus, in a strict manner, the behavior is always emergent since it cannot be reduced to a simplified internal mechanism: it is always the result of the interaction system-environment. In this sense, Pfeifer Iida and Bongard [16] indicate that emergence ceases to be a phenomenon with discrete characteristics (that is emergent or not emergent) and becomes as a matter of "emergence level": the less influence the designer's choices has on the current behavior of the agent, the higher is the emergence level. The systems developed to demonstrate an emergent behavior are usually more robust and adaptive. A system, such as genetic algorithms, that specifies the initial conditions and mechanisms for development (learning) will automatically explore the environment in order to shape its cognitive structure [16].

Another interesting agent design principle, named "three constituents", highlights the importance that any autonomous system should never be designed in isolation [16]. Froese and Ziemke [12] point out that we must consider three components of the system that are correlated: (i) the activity field or environment, (ii) the purpose and desired behavior, and (iii) the agent itself. These three components lead us to a clear intersection with the autopoietic approach. Furthermore, Froese and Ziemke also propose that in order to better understand the intelligence phenomenon we must think the agent as a holistic system rather than study its internal components in isolation. Of course it does not invalidate the development of the components individually, but to Froese and Ziemke, if we want to attain a greater scientific understanding of intelligence we must investigate how the adaptive behavior emerges holistically from the dynamic brain-body-world. Still on this subject, Pfeifer and Gomez [15] also indicate that the agents must be autonomous, self-sufficient, embodied and situated in a particular context.

4.1. Autopoiesis and knowledge representation

We do not intent to present a review on the foundations of knowledge representation. Such review is widely offered by Lakemeyer and Nebel in [17]. We will assume that the agent's intelligent behavior requires knowledge acquisition, storage and processing. To make it possible, it is essential to represent it. According to Elaine Rich and Kevin Knight [18], knowledge must be represented in such way that: (i) capture generalizations, identifying and gathering relevant properties, (ii) be understandable for people who provide it, (iii) be easily modifiable to allow error correction, reflect environmental changes, (iv) can be used in different situations even if incomplete or

inaccurate, (v) help to overcome their own data volume, helping to limit the number of possibilities that should be considered.

To the machine, this symbolic pattern should be consistent enough to generate an abstraction of the domain where it is embedded. This abstraction allows it to perform operations on these patterns in order to achieve problem's potential solutions. This set of symbolic patterns, in turn, may alter its collection of patterns, which consist in the agent's knowledge base, through internal processes, in an autopoietic way. It means that its internal processes, self-contained in the autopoietic machine, can only change the internal organization of this set of symbolic patterns. We might say, relying on Maturana and Varela, that the autopoietic machine is a self-homeostatic system that has its own organization as a variable that remains constant. The autopoietic organization means that processes concatenated in a particular manner such that these processes produce the components of the system and specify it as a unit.

Kenneth Craik [19] specified three fundamental steps for defining an agent-based knowledge: (i) the stimulus must be translated into an internal representation; (ii) cognitive processes manipulate the representation to derive new internal representations; (iii) these internal representations are translated into stimulus.

Most of the techniques found in literature represents knowledge explicitly through abstractions and use some kind of heuristic to achieve intelligent behavior. However, alternative approaches to GOFAI, such as ANN and GA, are interesting because they bring other non-explicit knowledge representation possibilities. We should highlight that even though non-explicit knowledge is used, disregarding the need for logic, syntactic or semantic knowledge structuring, it also needs to be structured in some way. We might, therefore, consider how the agent will be able to make its own infers, alter its owns perceptions and iterate with the environment, as a circular production network.

4.2. Emergence and creativity

In general, emergence designates a behavior that has not been explicitly programmed in a system or agent. Pfeifer and Bongard [13] point out three kinds of emergence: (i) a global phenomenon arising from a collective behavior, (ii) individual behavior as the result of an interaction between the agent and the environment and (iii) emergence behavioral from a time scale to another.

The ant-trail formation is an example of the first emergence kind. The ants, themselves, are unaware of the fact that they are forming a trail that will determine the shortest path to food. So when observing a population (even if it's artificial) we might focus on the dynamic emergent characteristics of this population.

The artistic installation named La Funambule Virtuelle [20], from Marie-Helene Tramus and Michel Bret, where a virtual acrobat evolves to keep up on a tightrope, reacting to the movements of the public. The character tries to reproduce the position of the iterator while trying to stay on the rope. In this installation, through an ANN, the balancer is able to learn to remain on the rope during the user interaction. From the learned gesture, a new behavior emerges through movements that were not taught, endowing the character of what the artist calls "the ability to improvise". This is a nice example of the individual behavior as the result of an interaction with the environment.

Finally, the third kind of emergence concerns time scales. They must be incorporated from three perspectives: (i) short-term, which regards current state of the mechanism, (ii) learning and development from the ontogenetic point of view and (iii) evolutionary, phylogenetic perspective. Therefore, the three time scales - short-term, ontogenetic and phylogenetic - should be considered in order to determine whether the system is able to demonstrate emergent behavior in any of these scales.

A deeper level of emergence called "epistemic emergence" involves, of course, the emergence of new perspectives intrinsically linked to the sensorial changes. The improvement or development of new sensorial organs allows an organism to evolve into another lineage, along with new world perspectives. This kind of development also occurs in our technological evolution as we build artifacts such as thermometers, clocks, telescopes, and that extend our senses or reactions as an extension of our natural biological functions.

5. Zer0 multiagent system

Zer0 is a game that invites the player to enjoy a drift in a universe ruled by geometric shapes, based on the Flow concept. According to Jen Chenova [21], people do associate many feelings with fun, like the sense of timelessness, of being at one, of exhilaration, focus, and immediacy. There is a universal agreement that without a dynamic balance between the challenge of an activity and the ability to meet that challenge, fun is something we are definitely not having.

According to Mihaly Csikszentmihalyi's [22] well-documented research and wide-scale gathering of personal observations, the phenomenology of Flow has eight major components:

1. A challenge activity that requires skills

2. The merging of action and awareness

3. Clear goals

4. Direct feedback

5. Concentration on the task at hand

6. The sense of control

7. The loss of self-consciousness

8. The transformation of time

5.1. Zer0 agents

In order to provide that fun experience, we've implemented a multi-agent system where each agent is visually represented by a pulsating geometric shape. Each shape has an internal clock that regulates its pulses. Every time a pulse intersects with another a sound event is generated, creating the game soundtrack.

There are basically two kinds of similar agents: user-controlled and autonomous. The second is highlighted in this paper, while the first is a slightly modified version of the autonomous one in order to allow the user control its movement. The characteristics of our autonomous agents are:

• Perception: O Position

O Other agents (through pulses) O Lifespan

• Actions O Pulse O Move O Stand

• Goals

O Increase lifespan O Move O Interact

• Environment

O Infinite 2D Space O Multi-agent

Each agent has an internal lifespan that is initialized randomly. Since the lifespan decreases, the individuals aim to expend their lifespan though the interaction with other shapes. Every time their pulses collide, both agents increase lifespan and earn points. The larger the amount of points, more geometrical "sides" the agent has. The user agent starts with one side (a single line), than evolves side by side: triangle, rectangle, pentagon and so on.

As we can see at figure 3, the agent perception is based on the perception component. This component informs the agent how is the world right now, including other agents that are nearby, its actual position and lifespan

Fig. 3. Internal autonomous agent generic architecture.

(internally represented). Based on this set of information it updates his internal world representation and then the inference machine reasons which action might be suitable.

The inference machine is based rule-based agent architecture. For example, if it is not time to generate a pulse (according to its internal clock) and there are no agents nearby, move.

5.2. Environment

As stated before, the agents are able to establish communication through pulses and each one of those pulses is a signal. Each time they interact, they increase their lifespan. These interactions trigger sound events, thus generating the game soundtrack. Those interactions are briefly represented in figure 4.

This environment might be described as partially observable, since it has a finite range of environment perception. Since the world's next stage depends on other factors than the agent's actions, it is stochastic. Also, this environment is constantly evolving while the agent is deliberating and there is no time interval. These two last characteristics impose some time constraints since the agent must answer quickly.

User can move or interact

Fig. 4.Two autonomous and one human agent represented in TROPOS Early Requirements initial diagram.

6. Conclusion

In this paper we've tried to show evidences that may help to clarify why the autopoiesis concept can be quite interesting for artists and scientists. Computer artists, especially, may find in this concept several technological challenges that might inspire them to produce artwork. AI theorists may find fascinating and inspiring the ontology behind what was presented. The papers that deal with interactivity, autonomy and creativity can be enriched when consider all aspects of autopoiesis and emergence.

The concept of emergence offer to the art and technology fields a heuristic for creativity. If emergence can be defined as pure novelty, then understanding the processes that lead to these events, structures, functions and emerging perspectives may be relevant to the construction of artifacts that use these processes to create newness. In this sense it is possible to design and implement algorithms based on natural emergent processes inorder to expand human creativity or construct artificial systems capable of demonstrate autonomous creativity.

In this experiment the visual representation and the game soundtrack emerge from a complex environment defined with simple rules. The agents evolve along with the environment, creating some kind of self-identity, required in order to reach an autopoietic level. That would be interesting to evolve these agents into more complex ones. For example, a BDI model could provoke huge changes in the musical score.

To conclude, it would be interesting to list some possible challenges for future investigation. The theoretical understanding of intelligent behavior would be one of them since despite more than half a century of research in AI, it still lacks a thorough under- standing of the mechanisms that controls, facilitates or enables intelligent behavior. This research aims to clarify this issue by the light of autopoiesis and emergence as foundations for cognition and intelligence.

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