Planning and Failure Detection

Multi-agent Systems, 2017

Multi-agent system (MAS) is an expanding field in science and engineering. It merges classical fields like game theory with modern ones like machine learning and computer science. This book provides a succinct introduction to the subject, covering the theoretical fundamentals as well as the latter developments in a coherent and clear manner. The book is centred on practical applications rather than introductory topics. Although it occasionally makes reference to the concepts involved, it will do so primarily to clarify real-world applications. The inner chapters cover a wide spectrum of issues related to MAS uses, which include collision avoidance, automotive applications, evacuation simulation, emergence analyses, cooperative control, context awareness, data (image) mining, resilience enhancement and the management of a single-user multi-robot.

SRI has built a domain-independent planning and execution system called CYPRESS which satisfies the following requirements: imperfect input information, rapid response during execution, minimal allocated computing resources during execution, incorporate preplanned plans, integrate planning and execution, and special technique for optimization of schedules. CYPRESS contains SIPE-2, a classical hierarchial Al planning system; PRS-CL, a reactive plan execution system; Gister-CL, a suite of evidential reasoning tools and the Grasper-CL system for interface and graphics. CYPRESS also runs in the ARPA/Rome Laboratory Planning Initiatives Common Prototyping Environment (CPE) and uses that system for communication between subsystems

Innovations in Applied Artificial …, 2005

planiart.usherbrooke.ca

To achieve a given goal, a mobile robot must plan by predicting the possible evolution of the environment and the possible consequences of its actions. The use of actuators and sensors with limited precision and the presence of exogenous agents in the environment leads to nondeterministic predictions. However, most planbased robotic frameworks ignore this nondeterminism at the planning time, by producing only deterministic plans, and replanning whenever the outcome of actions or the the environment's dynamics stray away from the assumed ones. The main motivation behind this choice has been the longstanding lack of effective planners handling nondeterminism; but recent advances in this area make it possible, and advisable, to exploit such systems to implement more robust planbased robot behaviors. In this paper, we experiment the integration of a state-of-the art contingency planner in a robotic architecture, and discuss how such an integration improves the degree of flexibility and robustness of plan-based robot behaviors compared to the use of a deterministic planner.

2003

We present an agent monitoring approach, which aims at refuting from (possibly incomplete) information at hand that a multi-agent system (MAS) is implemented properly. In this approach, agent collaboration is abstractly described in an action theory. Action sequences reaching the collaboration goal are determined by a planner, whose compliance with the actual MAS behavior allows to detect possible collaboration failures. The approach can be fruitfully applied to aid offline testing of a MAS implementation, as well as online monitoring. By well-known results, this is impossible in general but often also in simple cases if details of some agents (e.g., in a heterogenous environment) are missing.

Proceedings of the 4th Helenic Conference on Advances in Artificial Intelligence, 2006

Several agent frameworks have been proposed for developing intelligent software agents and multi-agent systems that are able to perform in dynamic environments. These frameworks and architectures exploit specific reasoning tasks (such as option selection, desire filtering, plan elaboration and means-end reasoning) that support agents to react, deliberate and/or interact/cooperate with other agents. Such reasoning tasks are realized by means of specific modules that agents may trigger according to circumstances, switching their behaviour between predefined discrete behavioural modes. This paper presents the facilities provided by the non-layered BDI-architecture of ICAGENT for supporting performance in dynamic and unpredictable multi-agent environments through efficient balancing between behavioural modes in a continuous space. This space is circumscribed by the purely (individual) reactive, the purely (individual) deliberative and the social deliberative behavioural modes. In a greater extend than existing frameworks; ICAGENT relates agent's flexible behaviour to cognition and sociability, supporting the management of plans constructed by the agent's mental and domain actions in a coordinated manner.

1997

Planning, the process of nding a course of action which can be executed to achieve some goal, is an important and well-studied area of AI. One of the central assumptions of classical AI-based planning is that after performing an action the resulting state can be predicted completely and with certainty. This assumption has allowed the development of planning algorithms that provably achieve their goals, but it has also hindered the use of planners in many real-world applications because of their inherent uncertainty. Recently, several planners have been implemented that reason probabilistically about the outcomes of actions and the initial state of a planning problem. However, their representations and algorithms do not scale well enough to handle large problems with many sources of uncertainty. This thesis introduces a probabilistic planning algorithm that can handle such problems by focussing on a smaller set of relevant sources of uncertainty, maintained as the plan is developed. This is achieved by using the candidate plan to constrain the sources of uncertainty that are considered, incrementally considering more sources as they are shown to be relevant. The algorithm is demonstrated in an implemented planner, called Weaver, that can handle uncertainty about actions taken by external agents, in addition to the kinds of uncertainty handled in previous planners. External agents may cause many simultaneous changes to the world that are not relevant to the success of a plan, making the ability t o determine and ignore irrelevant e v ents a crucial requirement for an e cient planner. Three additional techniques are presented that improve the planner's e ciency in a n umber of domains. First, the possible external events are analyzed before planning time to produce factored Markov c hains which can greatly speed up the probabilistic evaluation of the plan when structural conditions are met. Second, domainindependent heuristics are introduced for choosing an incremental modi cation to apply to the current plan. These heuristics are based on the observation that the failure of the candidate plan can be used to condition the probability that the modi cation will be successful. Third, analogical replay is used to share planning e ort across branches of the conditional plan. Empirical evidence shows that Weaver can create high-probability plans in a planning domain for managing the clean-up of oil spills at sea.

1994

This paper presents a new framework for autonomous agents that is based on the concept of anticipatory systems. It is a hybrid approach that synthesizes low-level reactive behavior and high-level symbolic reasoning. According to this framework, an agent, i.e. an anticipatory agent, consists of three main entities: a reactive system, a world model, and a meta-level component. The world model should, in addition to the description of the agent's environment, also include a description of the reactive part of the agent. The basic idea is that the meta-level component makes use of the world model to make predictions of future states. These predictions are then used by the meta-level to guide the agent's behavior on a high-level, whereas the low-level behavior is controlled by the reactive component.

1994

Agents situated in dynamic and unpredictable environments require several capabilities, including synthesizing and executing plans while continuing to be responsive to the world. The Cypress system is a domain-independent framework for defining persistent agents with this full range of behavior. Cypress has been used for several applications, including military operations and fault diagnosis on the Space Shuttle.