PredictiveModelingwithPolyagents
October2015
PredictiveModelingwithPolyagents
SystematicandEfficientExplorationofAlternativeTrajectoriesinSpaceandTime
Swarm Intelligence seeks to create artificially intelligent systems where
many individually simple active components (“agents”) self-organize to
provide the desired application functions as emergent features of the
overall system. Thereby it differs from traditional Artificial Intelligence
(AI) approaches where the application is realized with complex reasoning strategies at the individual level, which tends to limit the scalability of
such systems and requires significant knowledge engineering. Robust
self-organization of simple entities for emergent system-level functions
(i.e., not explicitly represented or reasoned over at the individual level) is
demonstrated in many large-scale systems in nature (Figure 1), ranging
from colonies of single-celled organisms that, under threatening environmental conditions, may act as a collective to ensure survival, through
many examples of social insect colonies, to the flocking behavior of
birds, fish, predators, and even crowds of humans.
Figure 1. Examples of Nature’s Swarming Systems.
Traditionally, applications engineered with Swarm Intelligence tend to deploy a
single swarm (collection of autonomous agents) from which the desired functionalities emerge (Figure 2). For instance, in swarm robotics, each robot is controlled by a single agent and complex feats (e.g., distributed persistent surveillance, material logistics) are achieved through the execution of the simple behavioral rules of each such agent. Large-scale self-organization of peer-to-peer network overlays, as for instance in the BitTorrent file-sharing system, also emerge
from the collective of identical client (agent) processes. But in applications, where
the choices of individual domain entities significantly affect overall outcomes
(e.g., humans in small groups), a single swarm is insufficient and a collection of
swarms is required.
The principals at AxonAI have many years of hands-on experience in the design
and implementation of polyagent models for various real-world applications. A
polyagent model comprises a collection of interacting swarms where each swarm
rep-resents a single application-relevant entity with complex behavior (Figure 3).
Figure 2. Applications with Single Swarms.
For instance, a polyagent model may be used to represent human actors in scenarios where the decisions of the individuals significantly affect the overall outcome (not as in crowd simulations). By representing each such actor with an entire swarm that interacts with other actors’ swarms, we have demonstrated the ability to capture complex individual behavior without sacrificing the engineering advantages (scalability, robustness, adaptability, etc.) that
the Swarm Intelligence approach offers.
Figure 3 illustrates a simple polyagent model for three domain entities (A (black), B
(red), and C (blue)) represented by three swarms. Each member of one swarm emulates a possible action sequence that its entity may perform based on a behavioral
model of the respective entity. That includes possible (inter-)actions with the environment (e.g., movement) and interactions with other entities represented by members of the other swarms. As there are many members to each swarm, many possible
scenarios may be extracted from the emergent patterns of the swarm dynamics. For
instance, we see 1) entity A possibly moving south and encountering entity B, 2) A
moving east, avoiding an obstacle by turning south and encountering C if C also
avoids the obstacle to the south (or missing C if it decides to go north around the
obstacle), 3) A avoiding the other entities altogether by moving north – and so on.
Figure 3. Polyagents = Interacting Swarms.
MarkSlonecker,President
[email protected]
©AxonAI,Inc.ProprietaryInformation.
Dr.SvenA.Brueckner,ChiefScientist
[email protected]
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PredictiveModelingwithPolyagents
October2015
A key element in polyagent models is the representation of time.
The emulation of possible action sequences by each agent implies a progression in time. While it is possible to deploy polyagent models without an explicit accounting for time (i.e., when
only short action sequences are considered and the dependencies among the entities are sparse), most relevant polyagent
models treat time as an equally important spatial dimension
within which action sequences are embedded.
Figure 4 illustrates the space-time embedding of the polyagents.
Architecturally, we define a single polyagent as the combination
of a single “Avatar” agent that persists as long as the domain
entity needs to be represented and a continuously renewed collection of “Ghost” agents that form the swarm for that entity in
our model. At a minimum, the avatar is the manager of its ghost
population, creating new ghosts and placing them in the shared
environment on a regular basis. Each ghost executes its behavioral model of the entity to generate action sequences that move
it in time until it reaches a given temporal distance from the avatar (e.g., “forecast horizon”). At that point, the ghost may perform some final reporting functions and then it removes itself
from the model.
Figure 4. Polyagent = 1 Persistent Avatar + Many Ephemeral
Ghosts. Embedded in Spatial Topology and (optional) Temporal
Dimension.
For polyagent models to avoid the combinatorial explosion that
would arise if each entity’s ghost interacted directly with all other entities’ ghosts, polyagent swarms interact only indirectly through the repeated manipulation of their shared environment.
Specifically, polyagent models maintain a collection of numerical markers indexed to spatio-temporal locations in the embedding topology that are the functional equivalent of the pheromones (volatile chemical substances) deposited and sensed by
insects (e.g., in the formation of paths among ants). Individual agents may read these markers in their local vicinity and incorporate them into their decision processes (pheromone sensing). They may also increment specific markers according to their
behavioral programming (pheromone deposits). Finally, the polyagents’ execution environment continuously decays non-zero
marker levels over time at fixed rates (pheromone evaporation). The disciplined application of deposit rules by agent populations and fixed-rate decay by the execution framework allows the designer of polyagent models to derive spatio-temporal
probability distribution for specific agent states or events such as for instance the likelihood that entity A and C will interact
south of the obstacle at a given point in time in Figure 3.
Polyagent models are well suited to systematically and concurrently explore many alternative futures a collection of domain
entities may realize. From a know state of the domain (e.g., the current
state), the model emulates possible decision sequences of the individuals
and the impact of any potential interactions to provide a quantitative
(probabilistic) assessment of future sys-tem states. Furthermore, by feeding back a utility assessment of individual ghost trajectories into the decision process of subsequently generated ghosts, the polyagent model may
generate not only the most plausible futures but also (alternative) paths
to the most desirable futures. Thus the model may serve as a decisionsupport tool that dynamically updates its recommendations as the state
of the domain changes. Finally, polyagent models may start their ghosts
in the recent past rather than at the current time and use evolutionary
tuning across parameterizations of the ghosts behavioral models against
a stream of recent entity observations to select for the most plausible
histories that may have taken the entities to their current state and provide refined behavioral models to extrapolate into the future. Figure 5
Figure 5. Entity Polyagent in Past and Future.
illustrates the spatio-temporal reasoning functions of polyagent models.
MarkSlonecker,President
[email protected]
©AxonAI,Inc.ProprietaryInformation.
Dr.SvenA.Brueckner,ChiefScientist
[email protected]
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