The Cognitive Aptitude of Swarming Agents
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The Cognitive Aptitude of Swarming Agents H. Van Dyke Parunak and Sven A. Brueckner Vector Research Center of TTGSI 3520 Green Court, Suite 250 Ann Arbor, MI 48105 USA +1 734 302 {4684, 4683} {van.parunak, sven.brueckner}@newvectors.net Abstract probes this issue. It draws together insights that we have previously published in a wide range of more Swarming models of computation, inspired by focused studies, blending them into a coherent social animals such as ants, are increasingly popular argument that swarming agents are a reasonable and in applications that require decentralized, distributed practical approach to constructing systems with human management of diverse entities in dynamic cognition. environments. However, the relative simplicity of We are not arguing that the human mind in fact digital ants compared with more conventional AI swarms. We strongly suspect that it does, but our claim agents sometimes leads to skepticism concerning the in this paper is much more modest. As builders of level of cognitive sophistication that such a system can computer systems to solve real-world problems, we produce. We explore three lines of argument that recommend swarming as a reasonable way to achieve swarms can execute cognition as well as other cognitive performance at least as acceptable as that computational methods: their intrinsic computational produced by other architectures. Aerospace engineers power, their ability to model the aggregate behavior of design successful airplanes without using the flapping human populations, and their representational wings that dominate natural flight. Cognitive behavior alignment with accepted models of individual need not be limited to architectures that mimic known cognition. Then we address practical issues in cognitive processes in humans. engineering swarms for realistic applications. We begin (Section 2) by summarizing one particular swarming idiom, marker-based stigmergic 1. Introduction coordination, that we have found particularly fruitful. The next four sections develop the argument that swarming agents using this mechanism have high There is growing interest among computer scientists cognitive aptitude, in three stages. in algorithms for distributed decentralized computing Section 3 argues that a population of stigmergic that are patterned on the behavior of social insects. agents is formally at least as powerful as a Turing These algorithms are most commonly used for machine, and thus we should not be surprised if it can applications similar to those addressed by those exhibit cognitive behavior as complex as any other animals, such as path planning (e.g., robot navigation computerized system. In principle, a swarm should be [23], packet routing for communications networks [8], able to do as well as any other computational cognitive TSP optimization[3]), sorting (data clustering [7]), or model at mimicking cognition. foraging (Kennedy’s swarm intelligence [9]). The next two sections make this abstract possibility We have applied these techniques to tasks that more concrete by comparing swarms with human would appear to require a higher level of cognition, cognition at two levels. Section 4 explores how a such as coordination of multiple agents [17] or battle swarm of simple agents can often generate group-level prediction [14]. The success of these systems is at first behavior similar to that of human groups (reflecting glance surprising, given the “obvious” difference in group cognition), while Section 5 explains why a cognitive capacity between ants and people, or swarm can faithfully mimic individual cognition. between simple swarming software agents and more It is one thing to claim that a swarm can exhibit complex agents that model the beliefs, desires, and high cognitive aptitude, and quite another to advocate intentions (BDI) of a human reasoner. This paper the use of swarms (rather than more direct cognitive node or one of its immediate neighbors. By analogy with insect prototypes, we sometimes refer to these variables as “digital pheromones.” The environment executes a set of processes on the fields. These typically include aggregation of contributions from multiple agents (a form of information fusion), propagation to neighboring nodes (a smoothing operation), and evaporation over time (thus discarding obsolete information). To reason over time, we maintain a set of field maps at discrete time steps (a “book of fields”) for the entire graph. Figure 1: Ghosts of one entity augment a labeled Each agent has state and a decision function. The scalar field reflecting their presence, and sense the state includes the agent’s location (typically a node of presence of ghosts of other entities through their the environment, though continuous coordinates are respective fields. also possible), and may also include domain-specific models) to implement cognitive tasks. Section 6 variables such as strength, wealth, or influence. The discusses some of the engineering aspects of executing inputs to the decision function are two vectors: the cognitive tasks with swarming. agent’s local state and the strengths of the fields on the agent’s current node and its neighbors in the graph. 2. Stigmergic Swarming The decision function has three outputs: changes in the agent’s state, the amount by which to augment the We define “swarming” as “useful self-organization various fields on the current node, and the next node to of multiple entities through local interactions” [11]. which the agent should move. Thus the decision This term is applicable to a number of different function maps from scalars to scalars, and is typically mechanisms, including interactions of individually implemented as a set of arithmetic equations, which intelligent agents [26], particle swarms [4], and can combine local field strengths using different coordination fields [10]. For concreteness, we focus the weights or even nonlinearly. discussion in this paper on swarming achieved by This basic machinery can be elaborated in a number marker-based stigmergy. “Stigmergy” refers to of ways. We most commonly use it in the polyagent coordination of multiple agents by means of a shared modeling construct [16], which represents each entity environment that they can sense and modulate. Particle in the domain by a set of agents (thus, a polyagent). A swarms and coordination fields are versions of persistent avatar manages a stream of transient ghosts , stigmergy in which the relevant characteristics of the each of which explores an alternative future for the environment are the locations and states of the agents entity in a simulated world. As the ghosts of different themselves. In marker-based stigmergy, the agents avatars interact via the fields they generate ( Figure 1), deposit and sense markers. they explore alternative futures for their individual A commonly cited example of marker-based entities, complete with the full range of possible stigmergy is the use of pheromones, chemical markers, interactions that might result from the alternative by social insects in tasks such as nest construction and futures of other entities. Each ghost dies after limited path marking. We generalize and formalize this lifetime, so avatars can generate them continuously process for application to real-world problems. Our without the need for garbage collection. systems include an environment, and a set of agents Stigmergic swarming is an attractive architecture that are localized in the environment and move over it. for domains with certain characteristics, including The environment has three components. • diversity of agent types, since the deposition and Structurally, it is a graph (a set of nodes with edges sensing of local scalar variables is a minimal among them). Depending on the application, the edges interface that places very few restrictions on an may be directed (as in a hierarchical task network) or agent’s inner architecture; undirected (as in a lattice representing the tiling of a • decentralized execution, since the feedback loops spatial region). The structure of the graph is an intrinsic to the architecture are conducive to self- important way to encode domain knowledge in a organizing and self-stabilizing behavior without a stigmergic system. A set of scalar variables is single authority; associated with each node in the environment, thus • distributed implementations that enable defining fields over the environment. Agents can applications to scale linearly with problem size, augment or decrement these variables when they are since each agent need interact only with a local located at the node, and sense them when they are at a neighborhood of environment nodes, and each of swarming agents can offer a credible model of the node need deal only with its neighbors and the aggregate behavior of a group of humans. This thesis is agents currently located on it; supported by two lines of evidence: the existence of • dynamic problems, since changes in the real universality in multi-agent modeling, and the observed world can be registered in the environment as they stigmergic behavior of real people. happen and appear the same to the agents as the changes being made by other agents, and the self- 4.1. Universality in Multi-Agent Modeling organizing dynamics of the system adjust to accommodate changed conditions. We use the term “universality” in analogy with its These benefits are attractive, but only if the use in statistical physics, where it refers to a curious architecture has the computational power to satisfy behavior