The concept of artificial life can take different meanings. In its current usage, the term artificial life (ALife) was coined in the late 1980s by Langton (1989), who originally defined it as “life made by man rather than by nature,” i.e., it is the study of man-made systems that exhibit behaviors characteristic of natural living systems. However, with time, Langton found fundamental problems with this definition, and redefined it as “the study of natural life, where nature is understood to include rather than to exclude, human beings and their artifacts” (Langton, 1998). He stated that human beings, and all that they do, are part of nature, and as such, a major goal of ALife should be to work toward removing “artificial life” as a phrase that differs in meaning in any fundamental way from the term “biology.” Indeed, it is now quite common for biologists to use computational models, which would have been considered as ALife 20 years ago, but now they are part of mainstream biology (Bourne et al., 2005).

Bedau (2007) defined contemporary artificial life as an interdisciplinary study of life and life-like processes, whose two most important qualities are that it focuses on the essential rather than the contingent features of living systems and that it attempts to understand living systems by artificially synthesizing simple forms of them. Three broad and intertwining branches of artificial life correspond to three different synthetic methods. “Soft” artificial life creates simulations or other purely digital constructions that exhibit life-like behavior (most ALife research is soft), “hard” artificial life produces hardware implementations of life-like systems, and “wet” artificial life synthesizes living systems from biochemical substances (Rasmussen et al., 2003, 2008). In this way, ALife attempts to synthesize properties of living systems in computers, machines, and molecules. Thus, ALife aims to understand biological life better by creating systems with life-like properties and developing novel forms of life.

In a broad sense, artificial life can be understood as the synthesis and simulation of living systems, which actually has been the name of the international workshops and conferences organized since 1987.

ALife has been an interdisciplinary research field (Langton, 1997; Adami, 1998; Dorin, 2014), bringing together biologists, philosophers, physicists, computer scientists, chemists, mathematicians, artists, engineers, and more. It has also been related to several fields, having a strong overlap with some of them, such as complexity (Bar-Yam, 1997; Mitchell, 2009), natural computing (de Castro, 2006), evolutionary computation (Baeck et al., 1997; Coello Coello et al., 2007), language evolution (Cangelosi and Parisi, 2002; Christiansen and Kirby, 2003), theoretical biology (Waddington, 1968a), evolutionary biology (Maynard Smith and Szathmáry, 1995), philosophy (Boden, 1996), cognitive science (Clark, 1997; Bedau, 2003; Couzin, 2009), robotics (Mataric and Cliff, 1996), artificial intelligence (AI) (Steels and Brooks, 1995)³, behavior-based systems (Maes, 1993; Webb, 2000), game theory (Sigmund, 1993), biomimesis (Meyer, 1997; Carmena et al., 2001), network theory (Newman, 2003; Newman et al., 2006), and synthetic biology (Benner and Sismour, 2005), among others.

Current ALife research can be classified into the 14 themes summarized in the rest of this section: origins of life, autonomy, self-organization, adaptation (evolution, development, and learning), ecology, artificial societies, behavior, computational biology, artificial chemistries, information, living technology, art, and philosophy. Figure 2 shows the number of papers published in the Artificial Life journal related to each of these themes since 1993. The first four themes focus more on properties of living systems. The next five themes study life at different scales. The last four are related to our understanding, uses, and descriptions we have of the living. This categorization is somewhat arbitrary, as several of the themes are entwined and overlapping. This also causes some of the topics to appear underrepresented, as related work has been mentioned in other subsections.

ALife has had a close relationship with the community of scientists working on the origins of life. Similar to the subdivision of ALife into two rather distinct areas focused on either individual autonomy or population evolution, there have been two major theories about the origin of life, known as the metabolism-first and replicator-first approaches (Dyson, 1985; Pross, 2004). The former typically views the origin of life as related to the emergence of self-producing and self-maintaining far-from-equilibrium structures, for example, based on the principles of autopoiesis (Ono et al., 2008), autocatalytic networks (Kauffman, 1986), and reaction-diffusion systems (Froese et al., 2012a). The latter approach, which has received more attention in mainstream science (Joyce, 2002), prefers to identify the origin of life with the beginning of evolution by natural selection (Tessera, 2009). Its classic formulation is the “RNA world” hypothesis (Gilbert, 1986), which has been generalized to the idea of natural selection in chemical evolution (Fernando and Rowe, 2007). In recent work, these two approaches can no longer be clearly distinguished as both autonomy and evolution are thought to be necessary for life (Ruiz-Mirazo and Moreno, 2004). Metabolism-first approaches have accepted the necessity of an informational capacity to enable open-ended evolution, even if it is in terms of a prebiotic “composite” genome (Segré et al., 2000). Replicator-first approaches, on the other hand, had to make recourse to membrane boundaries and metabolic activity, for example, to give rise to individuated protocells capable of competition (Chen et al., 2004). More recently, a new debate has arisen about the role of movement and adaptive behavior in the origin of life (Hanczyc, 2011; Egbert et al., 2012; Froese et al., 2014), a topic that had long been ignored by both metabolism- and replicator-first approaches. Indeed, one of the major open challenges in this area is to better understand the engineering of second-order emergence (Froese and Ziemke, 2009), that is, how to synthesize the underlying conditions for the emergence of an individual that, in interaction with its environment, gives rise to interesting behavior. Here, we therefore find the flipside of the problem faced by evolutionary robotics (see below); while models of the origin of life must somehow make its systems more interactive, robotics has to somehow make their systems more autonomous. It is likely that attempts at integrating biological autonomy, adaptive behavior, and evolution into one model will continue to improve, which would at the same time mean an integration of the various subfields of ALife. This integration of life and mind on various timescales is also supported by ongoing developments in the philosophy of mind and cognitive science, which is increasingly realizing the many ways in which mind is inseparable from a living body (Thompson, 2007).

A key question related to the studies of the origin of life is the definition of life itself (Schrödinger, 1944; Haldane, 1949; Margulis and Sagan, 1995; Bedau, 2008; Lazcano, 2008), to be able to determine when it began. Some argue that one of the defining properties of living systems is autonomy.

Since its beginnings, the field of ALife has always been closely associated with the concepts of biological autonomy and autopoiesis (Bourgine and Varela, 1992). The term “autopoiesis” was coined by the biologists Maturana and Varela (1980) to characterize a bounded network of processes that self-maintains its organization such that it is identifiable as a unity in the chemical domain. They created a computer model that can be considered as one of the first examples of ALife (Varela et al., 1974), and which has given rise to a tradition of computational autopoiesis in the field (McMullin, 2004). The precise definition of autopoiesis continues to be debated, and even Maturana and Varela were not always in agreement with each other (Froese and Stewart, 2010). Although the core idea seems to be that living beings are not only self-organizing but are also self-producing, they owe their existence as individual material entities to their ongoing internal (metabolic) and relational (regulatory) activities. This idea is sometimes formalized as operational closure, which can be defined as a network of processes in which each process enables, and is enabled by, at least one other process in that network. Varela (1979) used this concept to abstract autopoiesis from the specificities of the chemical domain so as to derive a concept of autonomy in general. In this way, Varela was able to describe other biological systems, such as the nervous system and the immune system, as being autonomous, even if they did not chemically self-produce. Relatedly, this concept of autonomy has been used to describe the self-sustaining dynamics of social interaction (De Jaegher and Froese, 2009). However, there is a concern that this abstraction makes us overlook what is essential to life itself, which has prompted some researchers to develop a more concrete theory of biological autonomy. For example, Ruiz-Mirazo and Moreno (2004) propose that “basic autonomy” is the capacity of a system to manage the flow of matter and energy through it so that it can regulate internal self-constructive and interactive exchange processes under far-from-equilibrium thermodynamic conditions.

This conception of autonomy, as referring to processes of self-production, must be distinguished from the term’s common use in robotics, where it is employed more loosely as the capacity of a system to move and interact without depending on remote control by an operator (Froese et al., 2007). Nevertheless, it is the strong sense of autonomy that allows us to talk about a system as being an individual that acts in relation to its intrinsic goals, i.e., of being a genuine agent (Barandiaran et al., 2009), rather than being a system whose functions are heteronomously defined from the outside. This has implications for how we should think about the so-called “ALife route to artificial intelligence” (Steels, 1993; Steels and Brooks, 1995). An important first step along this route was the development of behavior-based robotics, rather than micromanaging all aspects of a system’s behavior, as was common practice in good old-fashioned AI and still is in industrial robotics, behavior (see below) began to be seen as an emergent property of the robot-environment as a whole (Brooks, 1991).

Living systems need a certain degree of autonomy. This implies that they have certain control over their own production. This can be achieved through the process of self-organization.

The term “self-organizing system” was defined by Ashby (1947b) to describe phenomena where local interactions lead to global patterns or behaviors, such as in swarms, flocks, or traffic (Haken, 1981; Camazine et al., 2003; Gershenson and Heylighen, 2003; Gershenson, 2007). Early examples of self-organization in ALife include snowflakes [Packard (1986), p. 305–310] and boids (Reynolds, 1987), which are examples of models of pattern formation (Cross and Hohenberg, 1993) and collective motion (Vicsek and Zafeiris, 2012), respectively. There have also been several models of collective behavior (Couzin et al., 2004), such as flocks, schools, herds, and crowds.

Self-replication can be seen as a special case of self-organization, as a replicator has to conserve and duplicate its organization by itself. Examples from von Neumann to Langton have been already mentioned, although there have been several more (Sipper, 1998).

Another special case of self-organization is self-maintenance, which is related to homeostasis (Ashby, 1947a, 1960; Williams, 2006) and has been studied in relation to artificial chemistries (Ono and Ikegami, 1999, 2001) (see below).

Self-assembly (Whitesides and Grzybowski, 2002) can also be seen as a form of self-organization. There have been several examples in hard ALife of self-assembling or self-reconfigurating robots (Murata et al., 1994; Holland and Melhuish, 1999; Zykov et al., 2005; Dorigo et al., 2006; Støy and Nagpal, 2007; Ampatzis et al., 2009; Rubenstein et al., 2014; Werfel et al., 2014).

Some of these robots have taken inspiration from insect swarms. Their self-organization has served as an inspiration in computational intelligence (Bonabeau et al., 1999; Prokopenko, 2014a). More recently, these studies have been extended toward cognitive science (Trianni and Tuci, 2009; Gershenson, 2010). This kind of research is also related to collective intelligence (Hutchins, 1995) and the evolution of language (Steels, 2003).

Recent attempts to guide self-organization (Prokopenko, 2009, 2014b; Ay et al., 2012; Polani et al., 2013) are using information theory to develop systems, which are able to adapt to unforeseen circumstances (Gershenson, 2007).

Adaptation can be defined as “a change in an agent or system as a response to a state of its environment that will help the agent or system to fulfill its goals” (Gershenson, 2007). Adaptation is a central feature of living systems and is essential for autonomy and survival. One of the major criticisms of AI has been its lack of adaptability, as it traditionally attempted to predict and control rather than to adapt (Gershenson, 2013a), while part of ALife has focused on bringing adaptability to AI (Maes, 1993; Steels and Brooks, 1995). Still, both adaptability and predictability are desirable properties in natural and artificial systems.

Adaptation can occur at different time scales (Jablonka and Lamb, 2006; Gershenson, 2010). At a slow scale (several lifetimes), adaptation is called evolution. At a medium scale (one lifetime), adaptation is called development (including morphogenesis and cognitive development). At a fast scale (a fraction of a lifetime), adaptation is called learning. Adaptation at one or more scales has been a central topic in ALife, as shown by Figure 2.

At a high level of abstraction, ecological studies in ALife can be described as interactions between individuals from different species and with their environment.

Coevolution involves species interaction across generations, having strong relations with ecology. Sims’s creatures (Sims, 1994) are one example of coevolution. These creatures compete for a resource and evolve interesting morphologies and behaviors. A relevant topic within coevolution is the “red queen effect” (Dave and Miller, 1995) where species evolution affects the fitness of other species, leading to “arm races” (Nolfi and Floreano, 1998), which can promote the evolution of complex traits.

Also, related with evolution, ecological studies of ALife can offer insights into relationships such as symbiosis, parasitism (Watson et al., 2000; Froese et al., 2012a), and mutualism (Pachepsky et al., 2002).

At a global level, the living properties of biospheres have been studied. Perhaps the best known example is Daisyworld (Watson and Lovelock, 1983; Lenton and Lovelock, 2000). ALife models can study how regulation can occur as a consequence of multiple ecological interactions (McDonald-Gibson et al., 2008).

ALife ecological models, including cellular automata and agent-based (Grimm et al., 2005), have been used already in ecology for applications such as resource management (Bousquet and Page, 2004) and land-use models (Matthews et al., 2007), where models have to include also the social dimension.

Societies are defined by the interactions of individuals of the same species. The computational modeling of social systems has become very popular because it enables the systematic exploration of possibilities of social interaction, which are very difficult to achieve with complex societies (Gilbert and Conte, 1995; Epstein Axtell, 1996; Gershenson, 2001; Epstein, 2006).

For example, the evolution of cooperation has been a popular research topic (Burtsev and Turchin, 2006). Mainly based on game theory (Nowak, 2006), one of the most studied problems of cooperation is the prisoner’s dilemma (Santos et al., 2006, 2008). This approach has also been used to study multilevel selection (Traulsen and Nowak, 2006; Powers et al., 2011).

Central to human societies, the evolution of language and communication has been widely studied, beginning within the ALife community (Cangelosi and Parisi, 2002; Kirby, 2002; Steels, 2012). The evolution of language can be seen as a special case of semiotics, i.e., the problem of how meaning is acquired, which is also studied within ALife (Emmeche, 1991; Rocha, 1998; Ziemke and Sharkey, 2001) and closely related with philosophy (Gershenson, 2002).

Language is also a part of culture, which is beginning to be modeled within computational anthropology (Axtell et al., 2002).

The modeling of societies has led to the development of popular ALife games, such as Creatures (Grand, 2001) and The Sims (Wikipedia, 2014).

In several cases, artificial societies include models of individual behavior [e.g., Burtsev and Turchin (2006)].

Some of the differences between artificial intelligence and artificial life can be seen in their contrasting views of and approaches to synthesizing behavior. Put in somewhat simplified terms, AI reduces behavior to something that is specified to take place inside an agent independently and on its own terms. This internal processing is often implemented in terms of a sense-model-plan-act architecture, which means that the agents behavior has more to do with logical inferences based on internal representations rather than with interacting with the world in real time. This traditional view was widely criticized from scientific, engineering, and philosophical perspectives. These have agreed that the structure of behavior is primarily to be conceived, designed, and analyzed in terms of the dynamics of a closed sensorimotor loop (Braitenberg, 1986; Brooks, 1991; Cliff, 1991; Dreyfus, 1992; Clark, 1997; Pfeifer and Scheier, 1999; Pfeifer et al., 2007a). This has led to the study of adaptive behavior, mainly based on ethology (Maes, 1993; Meyer, 1997). This widespread paradigm shift made it evident that the contributions of the body and of the environment cannot be ignored, which is why this research is often referred to as embodied and situated (or embedded) cognition (Varela et al., 1991). Since the 1990s this paradigm has continued to grow in popularity (Wheeler, 2005; Chemero, 2009; Robbins and Aydede, 2009; Beer, 2014b), so much that the next step is to disentangle the many versions that have been proposed (Kiverstein and Clark, 2009). ALife has benefited from this paradigm shift because it has always preferred to study the conditions of emergence to pre-specified behavior, and because it has closely linked the notion of life with biological embodiment and its environment. As cognitive science is in the process of continuing its theoretical development from an embodied to a so-called “enactive” approach, which pays particular attention to the properties of the living such as autopoiesis, autonomy, and sense-making (Weber and Varela, 2002; Thompson, 2007; Di Paolo, 2009).Therefore, we can expect that ALife will take the place of AI as the most important synthetic discipline of cognitive science. It is ALife, not traditional AI, which has the tools in order to investigate the general principles of the biologically embodied mind (Di Paolo, 2003; Pfeifer et al., 2007b; Froese and Ziemke, 2009). At the same time, given the increasing interest in the science of consciousness, it is likely that these efforts will be complemented by a growing emphasis on synthesizing and using new kinds of immersive and life-like human-computer interfaces to explore life- and mind-as-it-could-be from the first-person perspective (Froese et al., 2012b).

Theoretical biology (Waddington, 1968b) preceded ALife in the abstract study of living systems. In return, ALife has contributed to theoretical biology with the development of computational models and tools.

Computers have enabled the study of complex systems (i.e., having many non-linearly interacting components), in a similar way as microscopes enabled microbiology (Pagels, 1989). Systems biology (Kitano, 2002) has also required computers to study the complexity of biological systems at different scales, overlapping with ALife in several aspects. The transmission, storage, and manipulation of information at different scales are essential features of living systems, and several ALife models focus on one or more of these.

Cellular automata were already mentioned (Wolfram, 1986; Wuensche, 1992). Similar models have been used to study other aspects of biology. For example, Kauffman (1969) proposed random Boolean networks as models of genetic regulatory networks (Aldana-González et al., 2003; Gershenson, 2004). Studying ensembles of such networks, the functional effects of topologies, modularity, degeneracy, and other structural properties can be measured (Gershenson, 2012), providing insights into the nature of adaptability and robustness. These models of genetic regulatory networks have been useful for theoretical biology, as they have demonstrated the role of criticality in evolution (Balleza et al., 2008) and suggested that a possible evolutionary mechanism for obtaining this criticality (Torres-Sosa et al., 2012).

The study of biological neural networks led to the proposal of several models of distributed computation (Rojas, 1996). Some of these have been used in ALife for the evolution, development, or learning of artificial “brains” with different applications.

In a similar way, the computational study of immune systems (Bersini, 1992; Forrest et al., 1994) has led to developments in computer security and optimization (Burke et al., 2014).

Artificial chemistries are used to study questions related to the origin of life from chemical components, as well as prebiotic and biochemical evolution (Dittrich et al., 2001). This is because chemical components are considered non-living, while they form living organisms. Perhaps the first computer simulation of the formation of a simple protocell consisting of a metabolic network and a boundary was that which introduced the concept of autopoiesis (Varela et al., 1974; Maturana and Varela, 1980; McMullin, 2004).

Other examples of work related to the transition from chemistry to biology include M, R systems (Rosen, 1958; Letelier et al., 2006), the chemoton (Gànti, 1975, 2003), the hypercycle (Eigen and Schuster, 1978, 1979), autocatalysis (Farmer et al., 1986; Kauffman, 1986), and algorithmic chemistry (Fontana, 1991).

Artificial chemistries have been extended to include evolution (Hutton, 2002) and are closely related with self-organization (Sayama, 2008).

It has been argued that living systems lie at the “edge of chaos” (Langton, 1990; Kauffman, 1993), i.e., they require a balance between stability/robustness and change/adaptability. How to find this balance? More generally, how are we to measure organization and self-organization? And adaptability, homeostasis, autonomy, or even autopoiesis? There have been several proposals, but still there is no agreement on how the properties of living systems should be measured.

A recent attempt has been to use information theory (Shannon, 1948; Prokopenko et al., 2009) to measure different properties of living systems. In this context, the field of guided self-organization is emerging (Prokopenko, 2009, 2014b; Ay et al., 2012; Polani et al., 2013), combining tools and concepts from information theory, self-organizing systems, and ALife.

For example, following Ashby’s law of requisite variety (Ashby, 1956), autopoiesis can be seen as the ratio of the complexity of a system over the complexity of its environment (Fernández et al., 2014). This implies that a living system requires a higher complexity than its environment to have a certain degree of autonomy. This view shifts the definition of life from “all or nothing” to a continuous transition between the non-living and living.

There have been hundreds of papers published on applications of ALife (Kim and Cho, 2006). More recently, the term “living technology” has been used to describe technology that is based on the core features of living systems (Bedau et al., 2009, 2013). Living technology is adaptive, robust, autonomous, and self-organizing. Living technology can be classified as primary and secondary [Bedau et al. (2009), p. 91]. Primary living technology is constructed from non-living components, while secondary living technology depends on living properties already present in its elements.

An example of primary living technology would be the design of protocells (Rasmussen et al., 2008) or artificial cells (Gibson et al., 2010) for applications such as cleaning pollution, generating energy, and improving health.

A broad area of application of secondary living technology lies within socio-technical systems (Helbing et al., 2012; Gershenson, 2013c). Governments, economies, and cities will be more efficient if they are “living,” i.e., exhibiting some of the key properties of living systems, potentially bringing numerous benefits to society.

ALife has the capacity to improve technologies, but also technologies have contributed to ALife. For example, there has been substantial ALife research based on the Internet, which facilitates the study of, e.g., interactive evolution (Taylor, 2014), which has also led to some artistic applications, e.g., Picbreeder (Secretan et al., 2008).

Within artificial intelligence, methods have been developed to model creativity (Boden, 1998). This has also been the case in ALife (Rinaldo, 1998; Whitelaw, 2004), where computational methods such as evolutionary computation have been used for creating artwork (McCormack and d’Inverno, 2012; Antunes et al., 2014), mainly within design, the visual arts, and music.

There have been several exhibitions dedicated to ALife art, such as the Ars Electronica Festival 1993, with many artists producing works within this movement (Penny, 2010). The VIDA Art and Artificial Life International Awards (Tenhaaf, 2008) began in 1999 and has been active since, supporting and promoting ALife art.

The interaction between the scientific and artistic ALife communities has been marginal and could be enhanced. Still, they are far more interconnected than is sometimes the case between the sciences and the humanities.

Artificial life has dealt with several philosophical questions (Boden, 1996). An ontology is required to discuss what life is. Epistemology is needed for understanding living systems (Pattee, 1995), but also artificial creatures can have their own epistemology (Beer, 2014a). ALife has also contributed to the philosophical discussions related to the nature of emergence (Bedau and Humphreys, 2008). Furthermore, building living systems has ethical implications (Bedau and Parke, 2009).

In particular, one unresolved question in the philosophy of artificial life is the status of the modeled phenomena. In the case of wet ALife, the synthetic creation of a living system logically implies the creation of an actual life form. But what about simulation models of living systems? Some researchers argue that since life is a property of the systemic organization of a material phenomenon (such as autopoiesis), and not identical with the material phenomenon per se, we should also treat modeled life as real life. This position is known as “strong” ALife. Still, it could also be argued that even though life is expressed by a certain systemic organization, it nevertheless requires a concrete material realization in order to be considered real life. On this view, modeled life is just that – a model and not real life. An intuitive way to understand this position (“weak” ALife) is to consider what happens when we run a program that is simulating the molecular structure of water. Although the formal organization of the molecules in the model is the same as of real water, the computer running the simulation does not get wet! Therefore, it becomes understandable why many researchers do not assign to their modeling results the same status as empirical data, that is, data obtained from wet ALife or other physical experiments. Yet, due to the complexity of most models, running a computer simulation can provide us with new insights, some of which may, in fact, be unattainable without actually running the simulation. In other words, models are not just computerized versions of thought experiments, they are “opaque” thought experiments (Di Paolo et al., 2000). This interpretation also connects the field of ALife with a long tradition in continental philosophy of mind that is currently gaining popularity in cognitive science, i.e., phenomenology (Gallagher and Zahavi, 2008), which also relies on imaginative thought experiments (a method known as eidetic variation) to investigate the essential structure of life and mind (Froese and Gallagher, 2010). Of course, this more conservative and pragmatic interpretation of the status of ALife models will not convince those who see life as a purely abstract relational phenomenon, and therefore, realizable by digital computers. Fortunately, for most purposes of scientific investigation based on the use of artificial life tools, this still unresolved philosophical debate is somewhat tangential. No matter whether we treat our simulations as models or as actual realizations, the objective results we obtain from them remain the same.