Planned synchronization for multi-robot systems with active observations

Multi-Agent Planning (MAP) is a topic of growing interest that deals with the problem of automated planning in domains where multiple agents plan and act together in a shared environment. In most cases, agents in MAP are cooperative (altruistic) and work together towards a collaborative solution. However, when rational self-interested agents are involved in a MAP task, the ultimate objective is to find a joint plan that accomplishes the agents' local tasks while satisfying their private interests. Among the MAP scenarios that involve self-interested agents, non-cooperative MAP refers to problems where non-strictly competitive agents feature common and conflicting interests. In this setting, conflicts arise when self-interested agents put their plans together and the resulting combination renders some of the plans non-executable, which implies a utility loss for the affected agents. Each participant wishes to execute its plan as it was conceived, but congestion issues and conflicts among the actions of the different plans compel agents to find a coordinated stable solution. Non-cooperative MAP tasks are tackled through non-cooperative games, which aim at finding a stable (equilibrium) joint plan that ensures the agents' plans are executable (by addressing planning conflicts) while accounting for their private interests as much as possible. Although this paradigm reflects many real-life problems, there is a lack of computational approaches to non-cooperative MAP in the literature. This PhD thesis pursues the application of non-cooperative games to solve non-cooperative MAP tasks that feature rational self-interested agents. Each agent calculates a plan that attains its individual planning task, and subsequently, the participants try to execute their plans in a shared environment. We tackle non-cooperative MAP from a twofold perspective. On the one hand, we focus on agents' satisfaction by studying desirable properties of stable solutions, such as optimality and fairness. On the other hand, we look for a combination of MAP and game-theoretic techniques capable of efficiently computing stable joint plans while minimizing the computational complexity of this combined task. Additionally, we consider planning conflicts and congestion issues in the agents' utility functions, which results in a more realistic approach. To the best of our knowledge, this PhD thesis opens up a new research line in non-cooperative MAP and establishes the basic principles to attain the problem of synthesizing stable joint plans for self-interested planning agents through the combination of game theory and automated planning.; La Planificacion Multi-Agente (PMA) es un tema de creciente interes que trata el problema de la planificacion automatica en dominios donde multiples agentes planifican y actuan en un entorno compartido. En la mayoria de casos, los agentes en PMA son cooperativos (altruistas) y trabajan juntos para obtener una solucion colaborativa. Sin embargo, cuando los agentes involucrados en una tarea de PMA son racionales y…

Restrictions on Antidepressant Medications for Children: A Review of Medicaid Policy

Collaboration of multiple intelligent agents on a shared task is a complex research issue, made particularly difficult when communication is limited or impossible. A common solution in multi-agent systems is to commit a team of collaborating agents to a joint plan. Since any deviation from the plan by an agent is hazardous, the common treatment of potential unplanned opportunities is either by ignoring them (even when actions increase the expected utility of the team), or by ad-hoc rules determining whether to accept such opportunities. Neither of these solutions is desirable. In our framework, Abstract World for Opportunistic Local decisions (AWOL for short), we attempt a disciplined treatment of opportunistic actions, in the context of an existing joint plan. The idea is to model the (stochastic) tradeoff of such opportunistic actions vs. continued commitment to the joint plan, while abstracting away as much as possible from the state of the world. The abstract model is evaluated using strict decision-theoretic criteria, with the goal of applying the optimal decision on whether to accept an opportunistic action in the original domain.

Generating joint plans for multiple self-interested agents is one of the most challenging problems in AI, since complications arise when each agent brings into a multi-agent system its personal abilities and utilities. Some fully centralized approaches (which require agents to fully reveal their private information) have been proposed for the plan synthesis problem in the literature. However, in the real world, private information exists widely, and it is unacceptable for a self-interested agent to reveal its private information. In this paper, we define a class of multi-agent planning problems, in which self-interested agents' values are private information, and the agents are ready to cooperate with each other in order to cost efficiently achieve their individual goals. We further propose a semi-distributed mechanism to deal with this kind of problems. In this mechanism, the involved agents will bargain with each other to reach an agreement, and do not need to reveal their private information. We show that this agreement is a possible joint plan which is Pareto optimal and entails minimal concessions.

Modular robotics deals with design and building of robots which can come together in versatile configurations to form a lattice shape in order to adapt to the task at hand. Each individual robot can either have different features or each have a generic form. The key feature is the ability of smaller, relatively simpler robots to come together to form larger formation, to perform complex tasks and to be multipurpose. Many robotic systems have been designed and proposed that can change their configuration and perform the task at hand. This paper presents the design, fabrication and results of a new modular, self-reconfigurable robotic cube called Mu-cubes. Each cube's edge is 6 cm in size, and magnetically bonded and autonomously driven by change in angular momentum. Inertial actuation consisting of BLDC motor, flywheel and braking mechanism impart an impulse of momentum which propel the cube in four different movements. The body of Mu-cube built with PMMA sheets of 6 mm thickness, laser cut into desired design. Diametrically oriented magnets are embedded in the edges to form magnetic hinges used in pivoting one cube with respect to other cube, while disc magnets are used to keep cubes aligned. This paper describes the Mu-cube unit's hardware design and fabrication along with results.

Combined state and parameter estimation of dynamical systems plays an important role in many branches of applied science and engineering. A wide variety of methods have been developed to tackle the joint state and parameter estimation problem. The Extended Kalman Filter (EKF) method is a popular approach which combines the traditional Kalman filtering and linearisation techniques to effectively tackle weakly nonlinear and non-Gaussian problems. Its mathematical formulation is based on the assumption that the probability density function (PDF) of the state vector can be reasonably approximated to be Gaussian. Recent investigations have been focused on Monte Carlo based sampling algorithms in dealing with strongly nonlinear and non-Gaussian models. Of particular interest is the Ensemble Kalman Filter (EnKF) and the Particle Filter (PF). These methods are robust in handling general forms of nonlinearities and non-Gaussian models, albeit with higher computational costs. In this paper we report the joint state and parameter estimation of noise-driven oscillatory systems undergoing limit cycle oscillation using EKF, EnKF and PF.

Autonomous underwater gliders use buoyancy control to achieve forward propulsion via a sawtooth-like, rise-and-fall trajectory. Because gliders are slow-moving relative to ocean currents, glider control must consider the effect of oceanic flows. In previous work, we proposed a method to control underwater vehicles in the (horizontal) plane by describing such oceanic flows in terms of streamlines, which are the level sets of stream functions. However, the general analytical form of streamlines in 3D is unknown. In this paper, we show how streamline control can be used in 3D environments by assuming a 2.5D model of ocean currents. We provide an efficient algorithm that acts as a steering function for a single rise or dive component of the glider's sawtooth trajectory, integrate this algorithm within a sampling-based motion planning framework to support long-distance path planning, and provide several examples in simulation in comparison with a baseline method. The key to our method's computational efficiency is an elegant dimensionality reduction to a 1D control region. Streamline-based control can be integrated within various sampling-based frameworks and allows for online planning for gliders in complicated oceanic flows.

This paper describes a series of experiments in creating autonomous drawing robots that generate aesthetically interesting and engaging drawings. Based on a previous method for multiple software agents that mimic the biological process of niche construction, the challenge in this project was to re-interpret the implementation of a set of evolving software agents into a physical robotic system. In this new robotic system, individual robots try to reinforce a particular niche defined by the density of the lines drawn underneath them. The paper also outlines the role of environmental interactions in determining the style of drawing produced.

Robotic automation is being increasingly proselytized in the industrial and manufacturing sectors to increase production efficiency. Typically, complex industrial tasks cannot be satisfied by individual robots, rather coordination and information sharing is required. Centralized robotic control and coordination is ill-advised in such settings, due to high failure probabilities, inefficient overheads and lack of scalability. In this paper, we model the interactions among robotic units using intelligent agent based interactions. The autonomous behavior of these agents requires task/resource allocation to be performed via distributed algorithms. We use the motivating example of warehouse inventory automation to optimally allocate and distribute delivery tasks among multiple robotic agents. The optimization is decomposed using primal and dual decomposition techniques to operate in minimal latency, minimal battery usage or maximal utilization scenarios. These techniques may be applied to multiple deployments involving coordination and task allocation between autonomous agents.

Research in Multi-Agent Planning (MAP) has traditionally been concerned with the design of coordination mechanisms such that the resulting joint plan meets the global goals of a problem. In contrast to most MAP techniques, in this paper we present a novel argumentation-based approach for multiple agents that plan cooperatively while having different capabilities, knowledge about the world and even contradictory information. Our aim is to enhance the role of argumentation as a means to attain a collective behaviour when devising a joint plan. Since agents’ decisions are influenced by the other agents’ plans, the use of mechanisms becomes relevant for persuading an agent to adopt a certain course of action, or negotiating on the use of scarce resources. Through a dialectical process, agents will discuss the different choices put forward by the others thus reaching a commonly agreed solution plan.

In this paper, we aim at modeling and analyzing the behavior of a spatial population of agents through an exploration of their state space. Agents are localized on a dynamic graph and they have internal states. They interact with an environment. The evolution of the agents and of the environment is specified by a set of rules. The framework is carefully designed to enable the construction of a global state space that can be automatically build and analyzed. The formalism, called IRNs for integrated regulatory networks, may be seen as an extension of logical regulatory networks (à la Thomas) developed in systems biology with spatial information and generalized to use arbitrary data values and update functions of this values. This thus allows to model systems with multiple agents that may be located on a varying spatial structure, may store and update local information, may depend on varying global information and may communicate in their neighborhood. A model of such a system can be defined as an IRN, and then analyzed using model-checking to asses its properties. This paper sketches the modeling framework and its semantics. We show how IRN may be used for the modeling of a population of simple agents, the automatic analysis of various reach ability properties and the use of symmetries to reduce the size of the state space.

The tuned vibration absorber (TVA) has been used for vibration control purposes in many sectors of civil/automotive/aerospace engineering for many decades since its inception by (Ormondroyd & Den Hartog, 1928). A tuned vibration absorber (TVA), in its most generic form, is an auxiliary system whose parameters can be tuned to suppress the vibration of a host structure. The auxiliary system is commonly a spring-mass-damper system (or equivalent) and the TVA suppresses the vibration at its point of attachment to the host structure through the application of an interface force. The tuned frequency a  of the TVA is defined as its undamped natural frequency with its base (point of attachment) blocked. The TVA can be used in two distinct ways, resulting in different optimal tuning criteria and design requirements (von Flotow et al., 1994): a. It can be tuned to suppress (dampen) the modal contribution from a specific troublesome natural frequency s  of the host structure over a wide band of excitation frequencies. b. It can be tuned to suppress (neutralise) the vibration at a specific troublesome excitation frequency  , in which case it acts like a notch filter. When used for application (a), the TVA referred to as a “tuned mass damper” (TMD). a  is optimally tuned to a value slightly lower than that of the targeted mode s  and an optimal level of damping needs to be designed into the absorber. When used for application (b), the TVA is referred to as a “tuned vibration neutraliser” (TVN) (Brennan, 1997, Kidner & Brennan, 1999) or “undamped TVA”. The optimal tuning condition is in this case is a    and the TVN suppresses the vibration over a very narrow bandwidth centred at the tuned frequency. Total suppression of the vibration at this frequency is achieved when there is no damping in the TVN. Deviation from the tuned condition (mistuning) degrades the performance of either variant of the TVA (von Flotow et al., 1994) and it can be shown that a mistuned vibration neutraliser could actually increase the vibration of its host structure (Brennan, 1997). To avoid mistuning, smart or adaptive tunable vibration absorbers (ATVAs) have been developed. Such devices are capable of retuning themselves in real time. Adaptive technology is especially important in the case of the TVN since the low damping requirement in the spring element can raise the host structure vibration to dangerous levels

In this paper, we consider multi‐agent constraint systems with preferences, modeled as soft constraint systems in which variables and constraints are distributed among multiple autonomous agents. We assume that each agent can set some preferences over its local data, and we consider two different criteria for finding optimal global solutions: fuzzy and Pareto optimality. We propose a general graph‐based framework to describe the problem to be solved in its generic form. As a case study, we consider a distributed meeting scheduling problem where each agent has a pre‐existing schedule and the agents must decide on a common meeting that satisfies a given optimality condition. For this scenario we consider the topics of solution quality, search efficiency, and privacy loss, where the latter pertains to information about an agent's pre‐existing meetings and available time‐slots. We also develop and test strategies that trade efficiency for solution quality and strategies that minimize information exchange, including some that do not require inter‐agent comparisons of utilities. Our experimental results demonstrate some of the relations among solution quality, efficiency, and privacy loss, and provide useful hints on how to reach a tradeoff among these three factors. In this work, we show how soft constraint formalisms can be used to incorporate preferences into multi‐agent problem solving along with other facets of the problem, such as time and distance constraints. This work also shows that the notion of privacy loss can be made concrete so that it can be treated as a distinct, manipulable factor in the context of distributed decision making.

The ideal of an “authoritative text” is no longer a taken-for-granted assumption among editorial and critical theorists of the literary text. Rather, texts are, and should be thought of as, composites of distributed activities of multiple social agents. This view has many virtues, but it quickly runs up against the deeply entrenched gestaltism of the “underlying work” stance, a perspective that invades some of the most commonplace ways of talking about Anglo-American literary texts. We explain the fundamental tensions that arise from this stance and offer a general ontology of literary artifacts that can account for the ways we habitually conceptualize texts and their effects. We provide a basic cognitive framework for understanding the ontology of the document, in its most generalized form, which can embrace a wide range of practices, literary and otherwise, that have significant implications for understanding editorial, authorial, and readerly behavior.

Ocean ecosystems have spatiotemporal variability and dynamic complexity that require a long-term deployment of an autonomous underwater vehicle for data collection. A new generation of long-range autonomous underwater vehicles (LRAUVs), such as the Slocum glider and Tethys-class AUV, has emerged with high endurance, long-range, and energy-aware capabilities. These new vehicles provide an effective solution to study different oceanic phenomena across multiple spatial and temporal scales. For these vehicles, the ocean environment has forces and moments from changing water currents which are generally on the order of magnitude of the operational vehicle velocity. Therefore, it is not practical to generate a simple trajectory from an initial location to a goal location in an uncertain ocean, as the vehicle can deviate significantly from the prescribed trajectory due to disturbances resulted from water currents. Since state estimation remains challenging in underwater conditions, feedback planning must incorporate state uncertainty that can be framed into a stochastic energy-aware path planning problem. This article presents an energy-aware feedback planning method for an LRAUV utilizing its kinematic model in an underwater environment under motion and sensor uncertainties. Our method uses ocean dynamics from a predictive ocean model to understand the water flow pattern and introduces a goal-constrained belief space to make the feedback plan synthesis computationally tractable. Energy-aware feedback plans for different water current layers are synthesized through sampling and ocean dynamics. The synthesized feedback plans provide strategies for the vehicle that drive it from an environment’s initial location toward the goal location. We validate our method through extensive simulations involving the Tethys vehicle’s kinematic model and incorporating actual ocean model prediction data.