Matching Items (4)
Description
Reasoning about actions forms the basis of many tasks such as prediction, planning, and diagnosis in a dynamic domain. Within the reasoning about actions community, a broad class of languages, called action languages, has been developed together with a methodology for their use in representing and reasoning about dynamic domains. With a few notable exceptions, the focus of these efforts has largely centered around single-agent systems. Agents rarely operate in a vacuum however, and almost in parallel, substantial work has been done within the dynamic epistemic logic community towards understanding how the actions of an agent may effect not just his own knowledge and/or beliefs, but those of his fellow agents as well. What is less understood by both communities is how to represent and reason about both the direct and indirect effects of both ontic and epistemic actions within a multi-agent setting. This dissertation presents ongoing research towards a framework for representing and reasoning about dynamic multi-agent domains involving both classes of actions.
The contributions of this work are as follows: the formulation of a precise mathematical model of a dynamic multi-agent domain based on the notion of a transition diagram; the development of the multi-agent action languages mA+ and mAL based upon this model, as well as preliminary investigations of their properties and implementations via logic programming under the answer set semantics; precise formulations of the temporal projection, and planning problems within a multi-agent context; and an investigation of the application of the proposed approach to the representation of, and reasoning about, scenarios involving the modalities of knowledge and belief.
The contributions of this work are as follows: the formulation of a precise mathematical model of a dynamic multi-agent domain based on the notion of a transition diagram; the development of the multi-agent action languages mA+ and mAL based upon this model, as well as preliminary investigations of their properties and implementations via logic programming under the answer set semantics; precise formulations of the temporal projection, and planning problems within a multi-agent context; and an investigation of the application of the proposed approach to the representation of, and reasoning about, scenarios involving the modalities of knowledge and belief.
ContributorsGelfond, Gregory (Author) / Baral, Chitta (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Lee, Joohyung (Committee member) / Moss, Larry (Committee member) / Cao Son, Tran (Committee member) / Arizona State University (Publisher)
Created2018
Description
This thesis describes a multi-robot architecture which allows teams of robots to work with humans to complete tasks. The multi-agent architecture was built using Robot Operating System and Python. This architecture was designed modularly, allowing the use of different planners and robots. The system automatically replans when robots connect or disconnect. The system was demonstrated on two real robots, a Fetch and a PeopleBot, by conducting a surveillance task on the fifth floor of the Computer Science building at Arizona State University. The next part of the system includes extensions for teaming with humans. An Android application was created to serve as the interface between the system and human teammates. This application provides a way for the system to communicate with humans in the loop. In addition, it sends location information of the human teammates to the system so that goal recognition can be performed. This goal recognition allows the generation of human-aware plans. This capability was demonstrated in a mock search and rescue scenario using the Fetch to locate a missing teammate.
ContributorsSaba, Gabriel Christer (Author) / Kambhampati, Subbarao (Thesis director) / Doupé, Adam (Committee member) / Chakraborti, Tathagata (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
This work describes an approach for distance computation between agents in a
multi-agent swarm. Unlike other approaches, this work relies solely on signal Angleof-
Arrival (AoA) data and local trajectory data. Each agent in the swarm is able
to discretely determine distance and bearing to every other neighbor agent in the
swarm. From this information, I propose a lightweight method for sensor coverage
of an unknown area based on the work of Sameera Poduri. I also show that this
technique performs well with limited calibration distances.
multi-agent swarm. Unlike other approaches, this work relies solely on signal Angleof-
Arrival (AoA) data and local trajectory data. Each agent in the swarm is able
to discretely determine distance and bearing to every other neighbor agent in the
swarm. From this information, I propose a lightweight method for sensor coverage
of an unknown area based on the work of Sameera Poduri. I also show that this
technique performs well with limited calibration distances.
ContributorsMulford, Philip (Author) / Das, Jnaneshwar (Thesis advisor) / Takahashi, Timothy (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2020
Description
The problem of modeling and controlling the distribution of a multi-agent system has recently evolved into an interdisciplinary effort. When the agent population is very large, i.e., at least on the order of hundreds of agents, it is important that techniques for analyzing and controlling the system scale well with the number of agents. One scalable approach to characterizing the behavior of a multi-agent system is possible when the agents' states evolve over time according to a Markov process. In this case, the density of agents over space and time is governed by a set of difference or differential equations known as a {\it mean-field model}, whose parameters determine the stochastic control policies of the individual agents. These models often have the advantage of being easier to analyze than the individual agent dynamics. Mean-field models have been used to describe the behavior of chemical reaction networks, biological collectives such as social insect colonies, and more recently, swarms of robots that, like natural swarms, consist of hundreds or thousands of agents that are individually limited in capability but can coordinate to achieve a particular collective goal.
This dissertation presents a control-theoretic analysis of mean-field models for which the agent dynamics are governed by either a continuous-time Markov chain on an arbitrary state space, or a discrete-time Markov chain on a continuous state space. Three main problems are investigated. First, the problem of stabilization is addressed, that is, the design of transition probabilities/rates of the Markov process (the agent control parameters) that make a target distribution, satisfying certain conditions, invariant. Such a control approach could be used to achieve desired multi-agent distributions for spatial coverage and task allocation. However, the convergence of the multi-agent distribution to the designed equilibrium does not imply the convergence of the individual agents to fixed states. To prevent the agents from continuing to transition between states once the target distribution is reached, and thus potentially waste energy, the second problem addressed within this dissertation is the construction of feedback control laws that prevent agents from transitioning once the equilibrium distribution is reached. The third problem addressed is the computation of optimized transition probabilities/rates that maximize the speed at which the system converges to the target distribution.
This dissertation presents a control-theoretic analysis of mean-field models for which the agent dynamics are governed by either a continuous-time Markov chain on an arbitrary state space, or a discrete-time Markov chain on a continuous state space. Three main problems are investigated. First, the problem of stabilization is addressed, that is, the design of transition probabilities/rates of the Markov process (the agent control parameters) that make a target distribution, satisfying certain conditions, invariant. Such a control approach could be used to achieve desired multi-agent distributions for spatial coverage and task allocation. However, the convergence of the multi-agent distribution to the designed equilibrium does not imply the convergence of the individual agents to fixed states. To prevent the agents from continuing to transition between states once the target distribution is reached, and thus potentially waste energy, the second problem addressed within this dissertation is the construction of feedback control laws that prevent agents from transitioning once the equilibrium distribution is reached. The third problem addressed is the computation of optimized transition probabilities/rates that maximize the speed at which the system converges to the target distribution.
ContributorsBiswal, Shiba (Author) / Berman, Spring (Thesis advisor) / Fainekos, Georgios (Committee member) / Lanchier, Nicolas (Committee member) / Mignolet, Marc (Committee member) / Peet, Matthew (Committee member) / Arizona State University (Publisher)
Created2020