From Artificial Life to Semiotic Agent Models: Review and Research Directions

Complex Systems Modeling Team
Computer Research and Applications Group (CIC-3)
Los Alamos National Laboratory, MS B265
Los Alamos, New Mexico 87545, USA
e-mail: or

Rocha, Luis M. [1999]. LANL Technical Report: LA-UR-99-5475

This paper is also available in Adobe Acrobat (.pdf) format from the LANL's Research Library's online catalog system.

Research effected with support from a project on decision structures of socio-technical organizations

Table of Contents.

1. Agents

The term agent is used today to mean anything between a mere subroutine to a conscious entity. There are "helper" agents for web retrieval and computer maintenance, robotic agents to venture into inhospitable environments, agents in an economy, etc. Intuitively, for an object to be referred to as an agent it must possess some degree of autonomy, that is, it must be in some sense distinguishable from its environment by some kind of spatial, temporal, or functional boundary. It must possess some kind of identity to be identifiable in its environment. To make the definition of agent useful, we often further require that agents must have some autonomy of action, that they can engage in tasks in an environment without direct external control. This leads us to an important definition of an agent from the XIII century, due to Thomas Aquinas: an entity capable of election, or choice.

This is a very important definition indeed; for an entity to be referred to as an agent, it must be able to step out of the dynamics of an environment, and make a decision about what action to take next - a decision that may even go against the natural course of its environment. Since choice is a term loaded with many connotations from theology, philosophy, cognitive science, and so forth, I prefer to discuss instead the ability of some agents to step out of the dynamics of its interaction with an environment and explore different behavior alternatives. In physics we refer to such a process as dynamical incoherence [Pattee, 1993]. In computer science, Von Neumann, based on the work of Turing on universal computing devices, referred to these systems as memory-based systems. That is, systems capable of engaging with their environments beyond concurrent state-determined interaction by using memory to store descriptions and representations of their environments. Such agents are dynamically incoherent in the sense that their next state or action is not solely dependent on the previous state, but also on some (random-access) stable memory that keeps the same value until it is accessed and does not change with the dynamics of the environment-agent interaction. In contrast, state-determined systems are dynamically coherent (or coupled) to their environments because they function by reaction to present input and state using some iterative mapping in a state space.

Let us then refer to the view of agency as a dynamically incoherent system-environment engagement or coupling as the strong sense of agency, and to the view of agency as some degree of identity and autonomy in dynamically coherent system-environment coupling as the weak sense of agency. The strong sense of agency is more precise because of its explicit requirement for memory and ability to effectively explore and select alternatives. Indeed, the weak sense of agency is much more subjective since the definition of autonomy, a boundary, or identity (in a loop) are largely arbitrary in dynamically coherent couplings. Since we are interested in simulations of decision-making agents, we need to look in more detail to agent based models with increasing levels of dynamical incoherency with their environments.

To summarize:

  1. Dynamically Coherent Agents
    1. Rely on subjective (spacial, functional, temporal) definition of autonomy. Function by reaction and are dynamically coupled to environments.
    2. Example: situated robots (wall-following machines), state-determined automata.
  2. Dynamically Incoherent Agents
    1. Possess models, syntax, language, decision-making ability. In addition to a level of dynamical coherence with their environments (material coupling), they possess an element of dynamical incoherence implemented by stable memory banks.
    2. Example: anything with symbolic memories and codes.

2. Agents: Holland's Complex Adaptive Systems

John Holland [1995] defines agents as rule-based input-output elements whose rules can adapt to an environment. These rules define the behavior strategy utilized by agents to cope with a changing environment. He also defines 7 basics or characteristics of agents and multi-agent sysems which further specify the rule-based adaptive behavior of agents:

  1. Aggregation (Property) has 2 senses
    1. Categorization (agent level): Agents cope with their environments by grouping things with shared characteristics and ignoring the differences.
    2. Emergence of large-scale behavior (multi-agent level): From the aggregation of the behavior of individual agents (e.g. Ants in an ant colony) we observe behavioral patterns of organization at the collective level. This leads to hierarchical organization.
  2. Tagging (Mechanism). Agents need to be individualized. They possess some identity. This in turn facilitates selection, specialization of tasks, and cooperation as different specific roles and strategies may be defined.
  3. Nonlinearity (Property). The integration or aggregation of agents in multi-agent systems is most often non-linear, in the sense that the resulting behavior cannot be linearly decomposed into the behavior of individual agents. This also implies that multi-agent systems lead to network causality, as effect and cause of agent behavior follow circular loops that cannot be linearly decomposed into traditional cause and effect chains.
  4. Flows (Property). Multi-agent systems rely on many connections between agents that instantiate the flow and transfer of interactions, information, materials, etc. Typically, the network of flows is represented with graphs.
  5. Diversity (Property). Typically, multi-agent systems are heterogeneous, as there exist different agent roles and behaviors. This makes the tagging mechanisms important so that these different roles and behaviors may be identified.
  6. Internal Models (Mechanism) organize the rules that produce agent behavior and can be used to let agents anticipate expected inputs from the environment. We can divide models in 2 types.
    1. Implicit: Prescribes a current action under implicit prediction. This is associated with hard-wired rules of behavior (e.g. by natural selection) and implemented by state-determined automata. This kind of model instantiates agents which are dynamically coupled to their environments, e.g. reactive, situated robots.
    2. Explicit: Use Representations stored in stable (or random access) memory to look ahead by exploring possible alternatives. This type of model produces agents with a level of dynamical incoherence with their environments, since they act not only based on current state and input but also by integrating information stored in memory. This integration can be pursued with more or less complicated reasoning procedures. Since agents with explicit models possess behavior alternatives, we can use them to study decision processes.
  7. Building blocks (Mechanism). Agents are built with less complicated components. This allows for the instantiation of coded construction, which is essential for the recombination of components to produce new agent with different behavior and models. Natural selection, for instance, acts on the ability to randomly vary descriptions of agents, which are cast on a language coding for building blocks leading to the production of new agents.

Holland's 7 basics lend themselves to our notions of dynamical incoherence, and therefore we can well use them to describe agents and (complex adaptive) multi-agent systems.

3. Review of Agent Models: From Strategies to Explicit Models and Beliefs

3.1 Encounters and Strategies

The agents in the models described in this section are based on game-theoretic strategies, using simple memory architectures. The environments in these models are defined exclusively by other agents, therefore there is really no level of dynamical coupling between agent and environment. Rather, these models aim to study only decision strategies and the evolution of strategies in an environment of other changing strategies. However, the strategies pursued by these agents rely on present state and a memory of only a small number of previous states and encounters. In addition, typically, the agent-rule updating is synchronous (all agents updated at the same time) and there is a determined behavior outcome. This results in dynamically coherent multi-agent systems, since agents cannot choose when and whether to participate, and their rules are determined by a short list of previous states.

3.1.1 Iterated Prisoner's Dilemma: Evolutionary Strategy Dynamics

Idealized model for real-world phenomena such as arm-races (Axelrod, 1984) and Evolutionary Biology (Maynard-Smith, 1982), iut of Game Theory as defined by Von Neumann, Economics, and Political Science. The dilemma is defined as follows: 2 individuals are arrested for committing a crime together are held in separate cells; no communication is allowed; both are offered the same deal to testify against the other (both know this); if one testifies (defects) gets a suspended sentence (S) and the other gets the total sentence (T); if both testify (defect), the testimony is discredited: both receive a heavy sentence (H); if neither one confesses (cooperate) both are convicted to a lesser sentence (L). These values must obey the following 2 conditions: (1) S>L>H>T and (2) 2L>S+T.

3.1.2 Extending the Prisoner's Dilemma

3.2 Learning and Evolution

The agents in the models described in this section possess more interesting environments with changing or non-trivial demands. The objective of these models is to study how learning and knowledge can interact with evolution.

The Baldwin Effect (organic selection): If learning helps the survival of organisms (more plastic behavior) then this trait should be selected. If the environment is fixed, so that the best things to learn remain fixed, the learned knowledge may be eventually genetically encoded via natural selection. Example: animals capable of learning to avoid a new predator or other environmental danger, will survive long enough to allow genetic variation to eventually discover that avoiding the danger is useful trait to possess at a instinctual, genetically determined level. Waddington [1942] referred to this process as genetic assimilation.

3.3 Evolution of Communication

Another class of models with interest to our problem area deals with the emergence and evolution of communication among agents with no explicitly programmed ability to communicate.

3.4 Agents with Shared Knowledge Structures

The last models of section 3.3 in their dealing with the evolution of communication, rely on the evolution of common structures among agents used to enable communication, e.g. artifactual structures. In this section we deal with models whose goal is to explicitly study the nature and consequences of such shared structure among agents. Being more explicit, these models emanate more from artificial intelligence, social science, and game theory, than from artificial life as the previous models did.

4. Developing Semiotic Agents

4.1 Design Requirements

4.1.1 Environments: The Selected Self-Organization Principle

In agent-based simulation agents interact in an artificial environment. However, it is often the case that the distinction between agents and environments is not clear. In contrast, in natural environments, the self-organization of living organisms is bound and is itself a result of inexorable laws of physics. Living organisms can generate an open-ended array of morphologies and modalities, but they can never change these laws. It is from these constant laws (and their initial conditions) that all levels of organization we wish to model, from life to cognition and social structure, emerge. These levels of emergence typically produce their own principles of organization, which we can refer to as rules, but all of these cannot control or escape physical law and are "neither invariant nor universal like laws" [Pattee, 1995b, page 27].

The question of what kinds of rules can emerge from deterministic or statistical laws is at the core of the field of Artificial Life [Langton, 1989]. It is also very much the question of generating and studying emergent semantics and decision processes in artificial environments - which we are interested in. However, "without principled restrictions this question will not inform philosophy or physics, and will only lead to disputes over nothing more than matters of taste in computational architectures and science fiction." [Pattee, 1995b, page 29] For agent-based simulations to be relevant for science in general, the same categories of laws/initial conditions and rules that we recognize in the natural world, need to be explicitly included in an artificial form. For more arguments for the need to explicitly distinguish laws and rules in artificial environments please refer to [Rocha and Joslyn, 1998].

Therefore, the setup of environments for multi-agent simulations needs to:

  1. Specify the dynamics of self-organization: specify laws and their initial conditions, which are responsible for the characteristics of the artificial environment (including agents) and the emergence of context-specific rules.
  2. Observe emergent or specify constructed semantics: identify emergent or pre-programmed, but changeable, rules that generate agent behavior in tandem with environmental laws. In particular, we are interested in the behavior of agents that can simulate semantics and decision processes.
  3. Provide a pragmatic selection criteria: create or identify a mechanism of selection so that the semantics identified in ii is grounded in a given environment. This selection criteria is based on constraints imposed both by the inexorable laws of the environment and the emergent rules. When based only on the first, we model an unchanging set of environmental demands (explicit selection), while when we include the second, we model a changing set of environmental demands instead (implicit selection).

A good example of experiments that use the three requirements above are the emergent computation experiments on Cellular Automata (CA) [Mitchell and Crutchfield, 1995] with Genetic Algorithms (GA's), described in section 3.3. Agents (each modeled by an automaton) are constrained to an one-dimensional lattice (the CA) and to a fixed automata production rule , which defines the lower-level virtual laws that lead to emergent behavior in such an environment. The emergent behavior produces different higher level structures to deal with environmental demands, sometimes producing an emergent semantics with a primordial syntax (e.g. particle computation): these are the higher-level, changing rules. Finally, there is an environment which requires a non-trivial task to be performed. The selection mechanism implemented by the GA is an explicit selection function, which directs the self-organization of rules from laws that cope well with the fixed environment.

These three requirements establish a selected self-organization principle [Rocha, 1996, 1998, 199?] observed in natural evolutionary systems. This principle is also essential to model the emergence of semantics and decision processes in agent-based simulations which can inform us about natural world phenomena. Essential because without an explicit treatment or understanding of these components in a simulation, it is impossible to observe which simulations results pertain to unchangeable constraints (laws), changeable, emergent, constraints (rules), and selective demands. It is often the case in Artificial Life computational experiments that one does not know how to interpret the results - is it life-as-it-could-be or physics-as-it-could-be? If we are to move these experiments to a modeling and simulation framework, then we need to establish an appropriate modeling relation with natural agent systems which are also organized according to laws, rules, and selection processes.

4.1.2 Agents

The design of semiotic agent models that we are interested in, build up some of the architectures presented in the review above. Semiotic agents, as we see them, need to be based on a few fundamental requirements:

  1. Asynchronous behavior. In our models, agents do not simultaneously perform actions at constant time-steps, like CA's or boolean networks. Rather, their actions follow discrete-event cues or a sequential schedule of interactions. The discrete-event setup allows for inter-generational transmission of information, or more generally, the cohabitation of agents with different environmental experience. The sequential schedule setup, formalized by Sequential Dynamical Systems (SDS) [Barrett et al, 1999], allows the study of different influence patterns among agents, very important to study decision processes in social networks. The latter are ideal for mathematical treatment as different schedules can be studied in the SDS framework, while the former require statistical experimentation as the collective behavior of discrete-event agents in an environment with stochastic laws and rules cannot be easily studied mathematically.
  2. Situated Communication. We require that communication among agents be based on the existence of environmental tokens and regularity which must follow the laws of the environment and agent rules. Communication must use resources available in the environment which follows laws and rules, and not rely on unconstrained, oracle-type, universal channels.
  3. Shared and cultural nature of language and knowledge. We require that agents share a certain amount of knowledge. This way, agents are not completely autonomous entities with their own understanding of their environments. We are interested in studying social systems which strongly rely on shared knowledge expressed in public languages. Often, in agent-based models, agents reach decisions resting solely on personal rules and knowledge-bases. This autonomous view of agency is unrealistic when it comes to modeling cognitive and social behavior, as ample evidence for the situated nature of cognition and culture [Clark, 1998; Richards et al, 1998; Beer, 1995; Rocha, 1999].
  4. Capacity to evaluate current status. Since a goal of agent-based simulations of social systems is to study decision processes, our agents need to include a means to describe their own preferences and beliefs. This way, agents need to have separate behavior components for action and evaluation. The evaluation component is used by the agent to judge its current status in the environment and then influence the action component. These components can be created and/or evolved independently. This way, we can model agents with different, independent beliefs about their present state and desirable goals.
  5. Stable, decoupled memory. To model more realistically decision processes, and achieve greater dynamical incoherence between agents and environments, we need to move from state-determined behavior components and endow agents with larger, random-access, memory capacity. This implies the storage of a set agents' interactions in memory to aid its evaluation and action behavior. These memory banks persist and can be accessed at any time by the agent, and do not depend on its current state or the state of the environment.

4.1.3 Knowledge: Dynamical Incoherence of Semiotic Agents

Given the environment and agent requirements of 4.1.1 and 4.1.2, we can now discuss the extent of the dynamical incoherency of semiotic agents as described in section 1. Clearly, the multi-agent systems with the requirements above possess elements of dynamical coherency and dynamical incoherency. The dynamic laws of the environment spawn rules of agent behavior which are this way dynamically coupled to the environment. The dynamical incoherence occurs with the following knowledge requirements:

  1. Shared knowledge structures which persist in the environment through at least for long intervals of dynamic production;
  2. Semantic tokens/artifacts required by situated communication, which persist in the environment, at least for long intervals of dynamic production, until they are picked up by agents;
  3. The stable memory banks used by agents to store knowledge are decoupled from the dynamics of the environment.

Note that the asynchrony requirement does not necessarily imply dynamical incoherence. Discrete-event or schedule-driven agents may or may not respond to their cue events or schedules in a dynamical coherent or incoherent manner. If their action and evaluation components are state determined, as the agents of Ackley and Littman or the SDS framework, then they are still dynamically coupled to their environment and its cues. It is only the knowledge requirements which can create a degree of dynamical incoherency, as memory gets decoupled from state-determined interaction.

4.2 Research Problems

  1. The asynchrony and situated communication agent requirements establish localized constraints on communication and event-driven actions among agents. We wish to investigate the nature of these constraints. We expect these constraints to be similar to the shared knowledge constraints of the model of Richards et al [1998]. Notice that this model is based on synchronous updating. We postulate that asynchrony and situated communication will result in the emergence of a shared knowledge structure with the same characteristics of Richards' et al model. Situated communication and asynchrony will implement the realistic situation of having agents' choice be dependent on the choice their neighbors have pursued earlier as well as on the events in their neighborhood. This would be an important theoretical result for the study of choice models.
  2. We wish to investigate the relative effect of the 5 agent design requirements on networks of influence in multi-agent social models. Particularly, different discrete-event and scheduling schemes will lead to different influence patterns. But, all other environment and agent requirements will play a role in these patterns.
  3. Partial shared knowledge structures. What is the effect of agents possessing only a subset of the overall shared knowledge structure in a given multi-agent system.
  4. We also wish to study possible disruption to communication and influence networks. For example, how dependent on situated communication tokens is the stability of a social structure?
  5. How does the size of the stable memory of agents affects the behavior and stability of the multi-agent system?
  6. How important is the evaluation component in a given multi-agent setup? How can it be influenced by other agents?

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1. Defect if opponent defected last, cooperate if opponent cooperated last. Start by cooperating.

For more information contact Luis Rocha at
Last Modified: September 02, 2004