Existence of Multiagent Equilibria with Limited Agents

Multiagent learning is a necessary yet challenging problem as multiagent systems become more prevalent and environments become more dynamic. Much of the groundbreaking work in this area draws on notable results from game theory, in particular, the concept of Nash equilibria. Learners that directly learn an equilibrium obviously rely on their existence. Learners that instead seek to play optimally with respect to the other players also depend upon equilibria since equilibria are fixed points for learning. From another perspective, agents with limitations are real and common. These may be undesired physical limitations as well as self-imposed rational limitations, such as abstraction and approximation techniques, used to make learning tractable. This article explores the interactions of these two important concepts: equilibria and limitations in learning. We introduce the question of whether equilibria continue to exist when agents have limitations. We look at the general effects limitations can have on agent behavior, and define a natural extension of equilibria that accounts for these limitations. Using this formalization, we make three major contributions: (i) a counterexample for the general existence of equilibria with limitations, (ii) sufficient conditions on limitations that preserve their existence, (iii) three general classes of games and limitations that satisfy these conditions. We then present empirical results from a specific multiagent learning algorithm applied to a specific instance of limited agents. These results demonstrate that learning with limitations is feasible, when the conditions outlined by our theoretical analysis hold

Coordination methodologies applied to RoboCup : a graphical definition of setplays

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 200

Enabling fast flexible planning through incremental temporal reasoning

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (leaves 70-71).In order for a team of autonomous agents to successfully complete its mission, the agents must be able to quickly re-plan on the fly as unforeseen events arise in the environment. This requires temporally flexible plans that allow the agent to adapt to execution uncertainties by not overcommitting on time constraints, and a continuous planner that replans at any point when the current plan fails. To achieve both of these requirements, planners must have the ability to reason quickly about timing constraints. This thesis provides a fast incremental algorithm, ITC, for determining the temporal consistency of temporally flexible plans. Additionally, the temporal reasoning capability of ITC is able to return the conflict or the nature of the inconsistency to the planner, such that the planner can resolve inconsistencies quickly and intelligently. The ITC algorithm combines the speed of shortest-path algorithms known to network optimization with the spirit of incremental algorithms such as Incremental A* and those used within truth maintenance systems (TMS). The algorithm has been implemented and integrated into a temporal planner, called Kirk. It has demonstrated an order of magnitude speed increase on cooperative air vehicle scenarios.by I-hsiang Shu.M.Eng

Planning for Distributed Execution Through Use of Probabilistic Opponent Models

In multiagent domains with adversarial and cooperative team agents, team agents should be adaptive to the current environment and opponent. We introduce an online method to provide the agents with team plans that a “coach ” agent generates in response to the specific opponents. The coach agent can observe the agents ’ behaviors but it has only periodic communication with the rest of the team. The coach uses a Simple Temporal Network(Dechter, Meiri, & Pearl 1991) to represent team plans as coordinated movements among the multiple agents and the coach searches for an opponent-dependent plan for its teammates. This plan is then communicated to the agents, who execute the plan in a distributed fashion, using information from the plan to maintain consistency among the team members. In order for these plans to be effective and adaptive, models of opponent movement are used in the planning. The coach is then able to quickly select between different models online by using a Bayesian style update on a probability distribution over the models. Planning then uses the model which is found to be the most likely. The system is fully implemented in a simulated robotic soccer environment. In several recent games with completely unknown adversarial teams, the approach demonstrated a visible adaptation to the different teams

Safe Distributed Coordination of Heterogeneous Robots through Dynamic Simple Temporal Networks

Research on autonomous intelligent systems has focused on how robots can robustly carry out missions in uncertain and harsh environments with very little or no human intervention. Robotic execution languages such as RAPs, ESL, and TDL improve robustness by managing functionally redundant procedures for achieving goals. The model-based programming approach extends this by guaranteeing correctness of execution through pre-planning of non-deterministic timed threads of activities. Executing model-based programs effectively on distributed autonomous platforms requires distributing this pre-planning process. This thesis presents a distributed planner for modelbased programs whose planning and execution is distributed among agents with widely varying levels of processor power and memory resources. We make two key contributions. First, we reformulate a model-based program, which describes cooperative activities, into a hierarchical dynamic simple temporal network. This enables efficient distributed coordination of robots and supports deployment on heterogeneous robots. Second, we introduce a distributed temporal planner, called DTP, which solves hierarchical dynamic simple temporal networks with the assistance of the distributed Bellman-Ford shortest path algorithm. The implementation of DTP has been demonstrated successfully on a wide range of randomly generated examples and on a pursuer-evader challenge problem in simulation

Optimización de la adquisición de modelos de oponentes en la Robocup

En este proyecto se tratará el tema del modelado de agentes, basándose en la extracción y representación del conocimiento sobre el comportamiento de un agente en un entorno competitivo. En este tipo de dominios en los que otros agentes interfieren en el cumplimiento de las metas, Riley y Veloso aseguran que los agentes han de ser capaces de adaptarse al comportamiento de los oponentes si pretenden tener éxito. Este proyecto se centra en el entorno futbolístico, partiendo de las bases definidas por la competición RoboCup, una iniciativa de investigación y educación a nivel internacional que provee una competición de fútbol que se realiza anualmente, y que cuenta con un amplio respaldo por parte de múltiples universidades y asociaciones. El objetivo final de este proyecto es determinar la influencia que puede tener el uso del modelo de un oponente a la hora de interactuar con otro agente. Parte de una idea existente, que es el trabajo realizado en la tesis doctoral “Aprendizaje automático en conjuntos de clasificadores heterogéneos y modelado de agentes”, y continúa el trabajo en el punto donde terminó. La forma de determinar dicha influencia ha sido a través de la evolución de un agente de tipo delantero adaptándose al comportamiento de un agente de tipo portero a través de la información obtenida a través de la observación. Este proyecto propone la utilización de una serie de herramientas creadas específicamente para optimizar dicho proceso, y gracias a las utilidades que proporciona la RoboCup este tipo de procedimientos son fácilmente verificables, lo cual ayudará a la hora de determinar si el agente al que se ha añadido el modelo del oponente se ha adaptado mejor o no al entorno.Ingeniería en Informátic

Network-centric automated planning and execution

Web services provide interoperability to network hosts with different capabilities. Complex tasks can be performed by composing services, assuming sufficient service descriptions are provided. Researchers are just beginning to realize the importance of accounting for network properties during automated service composition. The work presented in this thesis considers dynamic, heterogeneous networks—one type of network-centric environment.The purpose of this research is to improve network-centric service composition. This is accomplished by converting the service composition problem to an automated planning under uncertainty problem and by reasoning about network properties at various stages of the planning process. This thesis presents a method of improving the agents’ ability to construct, execute, and monitor plans in network-centric environments.There are two main contributions of this thesis: 1) generating qualitatively-different plans and 2) creating network-aware agents. As part of the former contribution, this thesis presents a comparison of methods used to create classical planning domains for distributed service composition problems. The other part of this contribution is an algorithm for guiding a plan-space planner to create qualitatively-different plans based on domain-dependent and network-centric plan evaluations. The second contribution pertains to network-awareness, which agents exhibit by reacting to changes in network conditions. This thesis describes methods of incorporating network-awareness into agents that 1) create plans, 2) execute plans, and 3) monitor plan execution.Experiments to validate the aforementioned contributions are presented in the context of an Improvised Explosive Device (IED) detection scenario. Several locations are monitored for IEDs using a variety of techniques including manual searching and visual change detection, as well as a variety of resources including humans, robots, and unmanned aerial vehicles (UAVs). Empirical results indicate that incorporating network-awareness into agents in dynamic, heterogeneous networks improves the overall service composition performance and effectiveness.M.S., Computer Science -- Drexel University, 200

Distributed Method Selection and Dispatching of Contingent, Temporally Flexible Plans

SM thesisMany applications of autonomous agents require groups to work in tight coordination. To be dependable, these groups must plan, carry out and adapt their activities in a way that is robust to failure and to uncertainty. Previous work developed contingent, temporally flexible plans. These plans provide robustness to uncertain activity durations, through flexible timing constraints, and robustness to plan failure, through alternate approaches to achieving a task. Robust execution of contingent, temporally flexible plans consists of two phases. First, in the plan extraction phase, the executive chooses between the functionally redundant methods in the plan to select an execution sequence that satisfies the temporal bounds in the plan. Second, in the plan execution phase, the executive dispatches the plan, using the temporal flexibility to schedule activities dynamically.Previous contingent plan execution systems use a centralized architecture in which a single agent conducts planning for the entire group. This can result in a communication bottleneck at the time when plan activities are passed to the other agents for execution, and state information is returned. Likewise, a computation bottleneck may also occur because a single agent conducts all processing.This thesis introduces a robust, distributed executive for temporally flexible plans, called Distributed-Kirk, or D-Kirk. To execute a plan, D-Kirk first distributes the plan between the participating agents, by creating a hierarchical ad-hoc network and by mapping the plan onto this hierarchy. Second, the plan is reformulated using a distributed, parallel algorithm into a form amenable to fast dispatching. Finally, the plan is dispatched in a distributed fashion.We then extend the D-Kirk distributed executive to handle contingent plans. Contingent plans are encoded as Temporal Plan Networks (TPNs), which use a non-deterministic choice operator to compose temporally flexible plan fragments into a nested hierarchy of contingencies. A temporally consistent plan is extracted from the TPN using a distributed, parallel algorithm that exploits the structure of the TPN.At all stages of D-Kirk, the communication load is spread over all agents, thus eliminating the communication bottleneck. In particular, D-Kirk reduces the peak communication complexity of the plan execution phase by a factor of O(A/e'), where e' is the number of edges per node in the dispatchable plan, determined by the branching factor of the input plan, and A is the number of agents involved in executing the plan.In addition, the distributed algorithms employed by D-Kirk reduce the computational load on each agent and provide opportunities for parallel processing, thus increasing efficiency. In particular, D-Kirk reduces the average computational complexity of plan dispatching from O(eN^3) in the centralized case, to typical values of O(eN^2) per node and O(eN^3/A) per agent in the distributed case, where N is the number of nodes in the plan and e is the number of edges per node in the input plan.Both of the above results were confirmed empirically using a C++ implementation of D-Kirk on a set of parameterized input plans. The D-Kirk implementation was also tested in a realistic application where it was used to control a pair of robotic manipulators involved in a cooperative assembly task