Monitoring using Heterogeneous Autonomous Agents.

This dissertation studies problems involving different types of autonomous agents observing objects of interests in an area. Three types of agents are considered: mobile agents, stationary agents, and marsupial agents, i.e., agents capable of deploying other agents or being deployed themselves. Objects can be mobile or stationary. The problem of a mobile agent without fuel constraints revisiting stationary objects is formulated. Visits to objects are dictated by revisit deadlines, i.e., the maximum time that can elapse between two visits to the same object. The problem is shown to be NP-complete and heuristics are provided to generate paths for the agent. Almost periodic paths are proven to exist. The efficacy of the heuristics is shown through simulation. A variant of the problem where the agent has a finite fuel capacity and purchases fuel is treated. Almost periodic solutions to this problem are also shown to exist and an algorithm to compute the minimal cost path is provided. A problem where mobile and stationary agents cooperate to track a mobile object is formulated, shown to be NP-hard, and a heuristic is given to compute paths for the mobile agents. Optimal configurations for the stationary agents are then studied. Several methods are provided to optimally place the stationary agents; these methods are the maximization of Fisher information, the minimization of the probability of misclassification, and the minimization of the penalty incurred by the placement. A method to compute optimal revisit deadlines for the stationary agents is given. The placement methods are compared and their effectiveness shown using numerical results. The problem of two marsupial agents, one carrier and one passenger, performing a general monitoring task using a constrained optimization formulation is stated. Necessary conditions for optimal paths are provided for cases accounting for constrained release of the passenger, termination conditions for the task, as well as retrieval and constrained retrieval of the passenger. A problem involving two marsupial agents collecting information about a stationary object while avoiding detection is then formulated. Necessary conditions for optimal paths are provided and rectilinear motion is demonstrated to be optimal for both agents.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111439/1/jfargeas_1.pd

Perceiving social interactions in the posterior superior temporal sulcus

Primates are highly attuned not just to social characteristics of individual agents, but also to social interactions between multiple agents. Here we report a neural correlate of the representation of social interactions in the human brain. Specifically, we observe a strong univariate response in the posterior superior temporal sulcus (pSTS) to stimuli depicting social interactions between two agents, compared with (i) pairs of agents not interacting with each other, (ii) physical interactions between inanimate objects, and (iii) individual animate agents pursuing goals and interacting with inanimate objects. We further show that this region contains information about the nature of the social interaction - specifically, whether one agent is helping or hindering the other. This sensitivity to social interactions is strongest in a specific subregion of the pSTS but extends to a lesser extent into nearby regions previously implicated in theory of mind and dynamic face perception. This sensitivity to the presence and nature of social interactions is not easily explainable in terms of low-level visual features, attention, or the animacy, actions, or goals of individual agents. This region may underlie our ability to understand the structure of our social world and navigate within it.National Science Foundation (U.S.) (Grant CCF-1231216

Comparing the Performance of SNMP and Web Services-Based Management

his paper compares the performance of Web services based network monitoring to traditional, SNMP based, monitoring. The study focuses on the ifTable, and investigates performance as function of the number of retrieved objects. The following aspects are examined: bandwidth usage, CPU time, memory consumption and round trip delay. For our study several prototypes of Web services based agents were implemented; these prototypes can retrieve single ifTable elements, ifTable rows, ifTable columns or the entire ifTable. This paper presents a generic formula to calculate SNMP’s bandwidth requirements; the bandwidth consumption of our prototypes was compared to that formula. The CPU time, memory consumption and round trip delay of our prototypes was compared to Net-SNMP, as well as several other SNMP agents. Our measurements show that SNMP is more efficient in cases where only a single object is retrieved; for larger number of objects Web services may be more efficient. Our study also shows that, if performance is the issue, the choice between BER (SNMP) or XML (Web services) encoding is generally not the determining factor; other choices can have stronger impact on performance

Endogenous Trading Networks

We investigate the effects of a class of trading protocols on the architecture and efficiency properties of endogenously formed trading networks. In our model, the opportunity to sell valuable objects occurs randomly to different individuals. A sale can only be realized if two individuals are connected, directly or indirectly, but forming and maintaining a trading relation is a costly investment. When the outcome of trading is efficient and provides no intermediation rents, a tension between equilibrium and efficient networks emerges when the cost of forming a link is at an intermediate level. There are two types of inefficiencies. Either all equilibrium networks are under- connected when compared to efficient networks, or a multiplicity of equilibriam may exist and agents may fail to coordinate on the efficient equilibrium network

May We Have Your Attention: Analysis of a Selective Attention Task

In this paper we present a deeper analysis than has previously been carried out of a selective attention problem, and the evolution of continuous-time recurrent neural networks to solve it. We show that the task has a rich structure, and agents must solve a variety of subproblems to perform well. We consider the relationship between the complexity of an agent and the ease with which it can evolve behavior that generalizes well across subproblems, and demonstrate a shaping protocol that improves generalization