Adaptive planning for distributed systems using goal accomplishment tracking

Goal accomplishment tracking is the process of monitoring the progress of a task or series of tasks towards completing a goal. Goal accomplishment tracking is used to monitor goal progress in a variety of domains, including workflow processing, teleoperation and industrial manufacturing. Practically, it involves the constant monitoring of task execution, analysis of this data to determine the task progress and notification of interested parties. This information is usually used in a passive way to observe goal progress. However, responding to this information may prevent goal failures. In addition, responding proactively in an opportunistic way can also lead to goals being completed faster. This paper proposes an architecture to support the adaptive planning of tasks for fault tolerance or opportunistic task execution based on goal accomplishment tracking. It argues that dramatically increased performance can be gained by monitoring task execution and altering plans dynamically

Applying autonomy to distributed satellite systems: Trends, challenges, and future prospects

While monolithic satellite missions still pose significant advantages in terms of accuracy and operations, novel distributed architectures are promising improved flexibility, responsiveness, and adaptability to structural and functional changes. Large satellite swarms, opportunistic satellite networks or heterogeneous constellations hybridizing small-spacecraft nodes with highperformance satellites are becoming feasible and advantageous alternatives requiring the adoption of new operation paradigms that enhance their autonomy. While autonomy is a notion that is gaining acceptance in monolithic satellite missions, it can also be deemed an integral characteristic in Distributed Satellite Systems (DSS). In this context, this paper focuses on the motivations for system-level autonomy in DSS and justifies its need as an enabler of system qualities. Autonomy is also presented as a necessary feature to bring new distributed Earth observation functions (which require coordination and collaboration mechanisms) and to allow for novel structural functions (e.g., opportunistic coalitions, exchange of resources, or in-orbit data services). Mission Planning and Scheduling (MPS) frameworks are then presented as a key component to implement autonomous operations in satellite missions. An exhaustive knowledge classification explores the design aspects of MPS for DSS, and conceptually groups them into: components and organizational paradigms; problem modeling and representation; optimization techniques and metaheuristics; execution and runtime characteristics and the notions of tasks, resources, and constraints. This paper concludes by proposing future strands of work devoted to study the trade-offs of autonomy in large-scale, highly dynamic and heterogeneous networks through frameworks that consider some of the limitations of small spacecraft technologies.Postprint (author's final draft

Multi-agent opportunism

The real world is a complex place, rife with uncertainty; and prone to rapid change. Agents operating in a real-world domain need to be capable of dealing with the unexpected events that will occur as they carry out their tasks. While unexpected events are often related to failures in an agent\u27s plan, or inaccurate knowledge in an agent\u27s memory, they can also be opportunities for the agent. For example, an unexpected event may present the opportunity to achieve a goal that was previously unattainable. Similarly, real-world multi-agent systems (MASs) can benefit from the ability to exploit opportunities. These benefits include the ability for the MAS itself to better adapt to its changing environment, the ability to ensure agents obtain critical information in a timely fashion, and improvements in the overall performance of the system. In this dissertation we present a framework for multi-agent opportunism that is applicable to open systems of heterogeneous planning agents. The contributions of our research are both theoretical and practical. On the theoretical side, we provide an analysis of the critical issues that must be addressed in order to successfully exploit opportunities in a multi-agent system. This analysis can provide MAS designers and developers important guidance to incorporate multi-agent opportunism into their own systems. It also provides the fundamental underpinnings of our own specific approach to multi-agent opportunism. On the practical side, we have developed, implemented, and evaluated a specific approach to multi-agent opportunism for a particular class of multi-agent system. Our evaluation demonstrates that multi-agent opportunism can indeed be effective in systems of heterogeneous agents even when the amount of knowledge the agents share is severely limited. Our evaluation also demonstrates that agents that are capable of exploiting opportunities for their own goals are also able, using the same mechanisms, to recognize and respond to potential opportunities for the goals of other agents. Further and perhaps more interesting, we show that under some circumstances, multi-agent opportunism can be effective even when the agents are not themselves capable of single-agent opportunism

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

Distributed Hybrid Simulation of the Internet of Things and Smart Territories

This paper deals with the use of hybrid simulation to build and compose heterogeneous simulation scenarios that can be proficiently exploited to model and represent the Internet of Things (IoT). Hybrid simulation is a methodology that combines multiple modalities of modeling/simulation. Complex scenarios are decomposed into simpler ones, each one being simulated through a specific simulation strategy. All these simulation building blocks are then synchronized and coordinated. This simulation methodology is an ideal one to represent IoT setups, which are usually very demanding, due to the heterogeneity of possible scenarios arising from the massive deployment of an enormous amount of sensors and devices. We present a use case concerned with the distributed simulation of smart territories, a novel view of decentralized geographical spaces that, thanks to the use of IoT, builds ICT services to manage resources in a way that is sustainable and not harmful to the environment. Three different simulation models are combined together, namely, an adaptive agent-based parallel and distributed simulator, an OMNeT++ based discrete event simulator and a script-language simulator based on MATLAB. Results from a performance analysis confirm the viability of using hybrid simulation to model complex IoT scenarios.Comment: arXiv admin note: substantial text overlap with arXiv:1605.0487

Virtuous and Adverse Selection within Economic Organizations

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ABC2 an agenda based multi-agent model for robots control and cooperation

This paper presents a model for the control of autonomous robots that allows cooperation among them. The control structure is based on a general purpose multi-agent architecture using a hybrid approach made up by two levels. One level is composed of reactive skills capable of achieving simple actions by their own. The other one uses an agenda used as an opportunistic planning mechanism to compound, activate and coordinate the basic skills. This agenda handles actions both from the internal goals of the robot or from other robots. This two level approach allows the integration of real-time response of reactive systems needed for robot low-level behavior, with a classical high level planning component that permits a goal oriented behavior. The paper describes the architecture itself, and its use in three different domains, including real robots, as well as the issues arising from its adaptation to the RoboCup simulator domai