Overview and Questions for Temporal Planning with Uncertainty in the Environment (original) (raw)

Design Concepts for a new Temporal Planning Paradigm

2012

Throughout the history of space exploration, the complexity of missions has dramatically increased, from Sputnik in 1957 to MSL, a Mars rover mission launched in November 2011 with advanced autonomous capabilities. As a result, the mission plan that governs a spacecraft has also grown in complexity, pushing to the limit the capability of human operators to understand and manage it. However, the effective representation of large plans with multiple goals and constraints still represents a problem. In this paper, a novel approach to address this problem is presented. We propose a new planning paradigm named HTLN, intended to provide a compact and understandable representation of complex plans and goals based on Timeline planning and Hierarchical Temporal Networks. We also present the design of a planner based on HTLN, which enables new planning approaches that can improve the performance of present real-world domains.

Planning under Continuous Time and Resource Uncertainty: A Challenge for AI

Cornell University - arXiv, 2012

We outline a cla�s of problems, typical of Mars rover operations, that are problematic for cur rent methods of planning under uncertainty. The existing methods fail because they suffer from one or more of the following limitations: 1) they rely on very simple models of actions and time, 2) they assume that uncertainty is manifested in discrete action outcomes, 3) they are only prac tical for very small problems. For many real world problems, these assumptions fail to hold. In particular, when planning the activities for a Mars rover, none of the above assumptions is valid: 1) actions can be concurrent and have dif fering durations, 2) there is uncertainty concern ing action durations and consumption of contin uous resources like power, and 3) typical daily plans involve on the order of a hundred actions. This class of problems may be of particular in terest to the UAI community because both clas sical and decision-theoretic planning techniques may be useful in solving it. We describe the rover problem, discuss previous work on planning un der uncertainty, and present a detailed, but very small, example illustrating some of the difficul ties of finding good plans. 1 THE PROBLEM Consider a rover operating on the surface of Mars. On a given day, there are a number of different scientific obser vations or experiment� that the rover could perform, and these are prioritized in some fashion (each observation or

A Probabilistic Approach to Robust Execution of Temporal Plans with Uncertainty

Lecture Notes in Computer Science, 2002

In Temporal Planning a typical assumption is that the agent controls the execu- tion time of all events such as starting and ending actions. In real domains how- ever, this assumption is commonly violated and certain events are beyond the di- rect control of the plan's executive. Previous work on reasoning with uncontrol- lable events (Simple Temporal Problem with Uncertainty) assumes that we can bound the occurrence of each uncontrollable within a time interval. In principle however, there is no such bounding interval since there is always a non-zero probability the event will occur outside the bounds. Here we develop a new more general formalism called the Probabilistic Simple Temporal Problem (PSTP) fol- lowing a probabilistic approach. We present a method for scheduling a PSTP maximizing the probability of correct execution. Subsequently, we use this method to solve the problem of finding an optimal execution strategy, i.e. a dy- namic schedule where scheduling decisions can be made on-line.

Planning under uncertainty in dynamic domains

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.

On representing planning domains under uncertainty

2010

Planning is an important activity in military coalitions and the support of an automated planning tool could help military planners by reducing the cognitive burden of their work. Current AI planning paradigms use two different types of formalism to represent the planning problem. Each of these formalisms entails different inference algorithms and representation of results.

Timeline-based Planning and Execution with Uncertainty: Theory, Modeling Methodologies and Practice

2019

Automated Planning is one of the main research field of Artificial Intelligence since its beginnings. Research in Automated Planning aims at developing general reasoners (i.e., planners) capable of automatically solve complex problems. Broadly speaking, planners rely on a general model characterizing the possible states of the world and the actions that can be performed in order to change the status of the world. Given a model and an initial known state, the objective of a planner is to synthesize a set of actions needed to achieve a particular goal state. The classical approach to planning roughly corresponds to the description given above. The timeline-based approach is a particular planning paradigm capable of integrating causal and temporal reasoning within a unified solving process. This approach has been successfully applied in many real-world scenarios although a common interpretation of the related planning concepts is missing. Indeed, there are significant differences among...

PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains

Journal of Artificial Intelligence Research, 2003

In recent years research in the planning community has moved increasingly towards application of planners to realistic problems involving both time and many types of resources. For example, interest in planning demonstrated by the space research community has inspired work in observation scheduling, planetary rover exploration and spacecraft control domains. Other temporal and resource-intensive domains including logistics planning, plant control and manufacturing have also helped to focus the community on the modelling and reasoning issues that must be confronted to make planning technology meet the challenges of application.

Strong Temporal Planning with Uncontrollable Durations: A State-Space Approach

Proceedings of the ... AAAI Conference on Artificial Intelligence, 2015

In many practical domains, planning systems are required to reason about durative actions. A common assumption in the literature is that the executor is allowed to decide the duration of each action. However, this assumption may be too restrictive for applications. In this paper, we tackle the problem of temporal planning with uncontrollable action durations. We show how to generate robust plans, that guarantee goal achievement despite the uncontrollability of the actual duration of the actions. We extend the state-space temporal planning framework, integrating recent techniques for solving temporal problems under uncertainty. We discuss different ways of lifting the total order plans generated by the heuristic search to partial order plans, showing (in)completeness results for each of them. We implemented our approach on top of COLIN, a stateof-the-art planner. An experimental evaluation over several benchmark problems shows the practical feasibility of the proposed approach.

Temporal Planning in Domains with Linear Processes

We consider the problem of planning in domains with continuous linear numeric change. Such change cannot always be adequately modelled by discretisation and is a key facet of many interesting problems. We show how a forward-chaining temporal planner can be extended to reason with actions with continuous linear effects. We extend a temporal planner to handle numeric values using linear programming. We show how linear continuous change can be integrated into the same linear program and we discuss how a temporal-numeric heuristic can be used to provide the search guidance necessary to underpin continuous planning. We present results to show that the approach can effectively handle duration-dependent change and numeric variables subject to continuous linear change.

Issues in temporal reasoning for autonomous control systems

Proceedings of the second international conference on Autonomous agents - AGENTS '98, 1998

Deep Space One will be the rst spacecraft to be controlled by an autonomous agent potentially capable of carrying out a complete mission with minimal commanding from Earth. The New Millennium Remote Agent (NMRA) includes a planner-scheduler that produces plans, and an executive that carries them out. In this paper we discuss several issues arising at the interface between planning and execution. including execution latency, plan dispatchability, and the distinction between controllable and uncontrollable events. Temporal information in the plan is represented within the general framework of Simple Temporal Constraint networks, as introduced by Dechter, Meiri, and Pearl. However, the execution requirements have a substantial impact on the topology of the links and the propagation through the network.