Partial-order Planning Research Articles

Revisiting block deordering in finite-domain state variable planning

Plan deordering removes unnecessary ordering constraints between actions in a plan, facilitating plan execution flexibility and several other tasks, such as plan reuse, modification, and decomposition. Block deordering is a variant of plan deordering that encapsulates coherent actions into blocks to eliminate further ordering constraints from a partial-order plan (POP) and is useful in many applications (e.g., generating macro-actions and improving the overall plan quality). The existing block deordering strategy is formulated in propositional encodings. Finite-domain state variable encodings (e.g., SAS+ representation), in contrast with propositional encodings, can capture the internal structure and the behavior of state variables of a planning instance through concise constructs such as causal graphs (CGs) and domain transition graphs (DTGs). This work redefines the semantics of block deordering terminologies and related plan deordering concepts in finite domain representation (FDR). Our proposed semantics also resolves some limitations of the existing block semantics and further enhance plan flexibility. In addition, this work exploits block deordering to eliminate redundant actions from a POP. A comparative analysis is also performed on block deordering with various deordering/reordering techniques using explanation-based order generalization (EOG) and MaxSAT. Our experiments on the benchmark problems from International Planning Competitions (IPC) show that our FDR formalism of block deordering significantly improves the plan execution flexibility while maintaining good coverage and execution time.

A hybrid approach for expressive numeric and temporal planning with control parameters

Since the release of PDDL 2.1, many planners accepted the challenge of addressing more complicated domains and problems, including both temporal and numeric features for scheduling and resources management. However, very few planners can handle a significant subset of the features that the language allows for modeling. To handle a level of expressiveness sufficient to work with more realistic problems, we have designed NextFLAP (Numeric EXpressions in TFLAP). The planner follows a hybrid approach, which combines a forward partial-order planner with a numeric constraint solver/optimizer. Additionally, we include a new feature, the control parameters, which are relevant for direct modeling numerical quantities and physical properties of objects in the actions, such as ‘partially charging of a battery’ or ‘obtaining a certain amount of resources’. This feature overcomes a limitation of the PDDL language, which is that the parameters of the actions must be objects (finite domain values) and, in no case, numeric variables. In this paper we illustrate the advantages of using these temporal and numeric modeling features and then describe our planning system, NextFLAP, that can handle these domains. We show that NextFLAP is competitive with other state-of-the-art planners and that it generates good-quality plans.

Integrating Story-Centric and Character-Centric Processes for Authoring Interactive Drama

Computer aided interactive drama has been widely applied for entertainment and pedagogy. Most existing approaches for authoring interactive drama use either story-centric or character-centric processes. In this work, we present a new framework that integrates both types of processes to support authoring. This framework uses a multi-agent system to control virtual characters in a story. The characters’ motivations are encoded as the agents’ goals, and are configured based on well-structured story paths generated using a partial order planner. This framework allows the use of a planner that has an equivalent model of the story as that in the multi-agent system or models the story at a more abstract level, e.g. only the plot points. We explore the use of this new framework for authoring interactive dramas. Preliminary examples of application are presented.

Generalizing and Executing Plans

In a dynamic environment, an intelligent agent must consider unexpected changes to the world and plan for them. We aim to address this key issue by building more robust artificial agents through the generalization of plan representations. Our research focuses on the process of generalizing various plan forms and the development of a compact representation which embodies a generalized plan as a policy. Our techniques allow an agent to execute efficiently in an online setting. We have, to date, demonstrated our approach for sequential and partial order plans and are pursuing similar techniques for representations such as Hierarchical Task Networks and GOLOG programs

The Complexity of Planning Revisited — A Parameterized Analysis

The early classifications of the computational complexity of planning under various restrictions in STRIPS (Bylander) and SAS+ (Bäckström and Nebel) have influenced following research in planning in many ways. We go back and reanalyse their subclasses, but this time using the more modern tool of parameterized complexity analysis. This provides new results that together with the old results give a more detailed picture of the complexity landscape. We demonstrate separation results not possible with standard complexity theory, which contributes to explaining why certain cases of planning have seemed simpler in practice than theory has predicted. In particular, we show that certain restrictions of practical interest are tractable in the parameterized sense of the term, and that a simple heuristic is sufficient to make a well-known partial-order planner exploit this fact.

Dynamic and Accelerated Partial Order Planning for Interactive Narratives

This paper explores new narrative generation paradigms for open world problems. We propose a speed-up variant of partial planner–accelerated partial order planner, that can automatically generate narratives for large plan spaces. To incorporate real-time free-form user interaction, a dynamic partial planning technique has been introduced to self-repair the narratives. We also propose a scalable and robust framework to craft open world narratives with minimal effort. Our approach enables content creators to craft complex open world narratives without explicitly authoring user interaction arcs. We tested our framework by developing multiple narratives with free-form interactions. Those narratives were used to test the robustness of the proposed planners.

Forward-Chaining Partial-Order Planning

Over the last few years there has been a revival of interest in the idea of least-commitment planning with a number of researchers returning to the partial-order planning approaches of UCPOP and VHPOP. In this paper we explore the potential of a forward-chaining state-based search strategy to support partial-order planning in the solution of temporal-numeric problems. Our planner, POPF, is built on the foundations of grounded forward search, in combination with linear programming to handle continuous linear numeric change. To achieve a partial ordering we delay commitment to ordering decisions, timestamps and the values of numeric parameters, managing sets of constraints as actions are started and ended. In the context of a partially ordered collection of actions, constructing the linear program is complicated and we propose an efficient method for achieving this. Our late-commitment approach achieves flexibility, while benefiting from the informative search control of forward planning, and allows temporal and metric decisions to be made - as is most efficient - by the LP solver rather than by the discrete reasoning of the planner. We compare POPF with the approach of constructing a sequenced plan and then lifting a partial order from it, showing that our approach can offer improvements in terms of makespan, and time to find a solution, in several benchmark domains.

Hybrid planning and distributed iterative repair for multi-robot missions with communication losses

This paper presents a planning and execution architecture suited for the initial planning, the execution and the on-board repair of a plan for a multi-robot mission. The team as a whole must accomplish its mission while dealing with online events such as robots breaking down, new objectives for the team, late actions and intermittent communications. We have chosen a “plan then repair” approach where an initial plan is computed offline and updated online whenever disruptive events happen. We have defined an hybrid planner that mixes Partial Order Planning (POP) with a Hierarchical Task Network (HTN)-based modelling of actions. This planner, called HiPOP for Hierarchical Partial-Order Planner, computes plans with temporal flexibility (thus easing its execution) and abstract actions (thus easing the repair process). It uses a symbolic representation of the world and has been extended with geometrical reasoning to adapt to multi-robots missions. Plans are executed in a distributed way: each robot is responsible of executing its own actions, and to propagate delays in its local plan, taking benefit from the temporal flexibility of the plan. When an inconsistency or a failure arises, a distributed repair algorithm based on HiPOP is used to repair the plan, by iteratively removing actions in the plan in order to amend the global plan. This repair is done onboard one of the robot of the team, and takes care of partial communication. The whole architecture has been evaluated through several benchmarks, statistical simulations, and field experiments involving 8 robots.

Player behavior and personality modeling for interactive storytelling in games

Current video games use simple methods to deal with interactive narratives and the enormous variety of player types. In this paper, we propose a novel approach to interactive storytelling in games, in which the quests and the ongoing story are determined in view of individual personality traits and behavioral attitudes in a nondeterministic way. Our method starts the process employing a new technique to assess the player’s personality traits according to the well-known Big Five model. These traits are then used by a nondeterministic planning algorithm to define adaptive goal hierarchies. In addition, an artificial neural network is trained to predict player behaviors in real-time, allowing partial-order planning operators to use player behaviors and personality traits as logical terms in their preconditions. With this approach, a richer individualized experience is provided to the player, while preserving consistency with the conventions of the chosen genre.

A configurable partial-order planning approach for field level operation strategies of PLC-based industry 4.0 automated manufacturing systems

The machine and plant automation domain is faced with an ever increasing demand for ensuring the adaptability of manufacturing facilities in context of Industry 4.0. Field level automation software plays a dominant role in strengthening the overall flexibility of manufacturing resources. Classical programming approaches based typically on signal-oriented languages result in disproportionate effort for ensuring necessary flexibility. To address this challenge, a novel approach based on artificial intelligence planning techniques is presented which is able to handle domain specific requirements while facilitating efficient, scalable problem solving. Throughout this article, a discussion of specific requirements on automated planning techniques for field level automation software in the machine and plant automation domain with respect to Industry 4.0 is provided. An intensive study on existing works and their drawbacks towards addressing these requirements is presented. The proposed configurable partial-order planning approach is based upon a combination of an adapted goal-based planning formulation and its reformulation by means of linear programming techniques. It is shown that the proposed approach is able to efficiently solve large planning problems by exhibiting positive scalability characteristics which indicates its applicability for real-size plants.

A planning approach to the automated synthesis of template-based process models

The design-time specification of flexible processes can be time-consuming and error-prone, due to the high number of tasks involved and their context-dependent nature. Such processes frequently suffer from potential interference among their constituents, since resources are usually shared by the process participants and it is difficult to foresee all the potential tasks interactions in advance. Concurrent tasks may not be independent from each other (e.g., they could operate on the same data at the same time), resulting in incorrect outcomes. To tackle these issues, we propose an approach for the automated synthesis of a library of template-based process models that achieve goals in dynamic and partially specified environments. The approach is based on a declarative problem definition and partial-order planning algorithms for template generation. The resulting templates guarantee sound concurrency in the execution of their activities and are reusable in a variety of partially specified contextual environments. As running example, a disaster response scenario is given. The approach is backed by a formal model and has been tested in experiments.

Plan Reordering and Parallel Execution — A Parameterized Complexity View

Bäckström has previously studied a number of optimization problems for partial-order plans, like finding a minimum deordering (MCD) or reordering (MCR), and finding the minimum parallel execution length (PPL), which are all NP-complete. We revisit these problems, but applying parameterized complexity analysis rather than standard complexity analysis. We consider various parameters, including both the original and desired size of the plan order, as well as its width and height. Our findings include that MCD and MCR are W[2]-hard and in W[P] when parameterized with the desired order size, and MCD is fixed-parameter tractable (fpt) when parameterized with the original order size. Problem PPL is fpt if parameterized with the size of the non-concurrency relation, but para-NP-hard in most other cases. We also consider this problem when the number (k) of agents, or processors, is restricted, finding that this number is a crucial parameter; this problem is fixed-parameter tractable with the order size, the parallel execution length and k as parameter, but para-NP-hard without k as parameter.

Parallel heuristic search in forward partial-order planning

AbstractMost of the current top-performing planners are sequential planners that only handle total-order plans. Although this is a computationally efficient approach, the management of total-order plans restrict the choices of reasoning and thus the generation of flexible plans. In this paper, we present FLAP2, a forward-chaining planner that follows the principles of the classical POCL (Partial-Order Causal-Link Planning) paradigm. Working with partial-order plans allows FLAP2 to easily manage the parallelism of the plans, which brings several advantages: more flexible executions, shorter plan durations (makespan) and an easy adaptation to support new features like temporal or multi-agent planning. However, one of the limitations of POCL planners is that they require far more computational effort to deal with the interactions that arise among actions. FLAP2 minimizes this overhead by applying several techniques that improve its performance: the combination of different state-based heuristics and the use of parallel processes to diversify the search in different directions when a plateau is found. To evaluate the performance of FLAP2, we have made a comparison with four state-of-the-art planners: SGPlan, YAHSP2, Temporal Fast Downward and OPTIC. Experimental results show that FLAP2 presents a very acceptable trade-off between time and quality and a high coverage on the current planning benchmarks.

Optimal Partial-Order Plan Relaxation via MaxSAT

Partial-order plans (POPs) are attractive because of their least-commitment nature, which provides enhanced plan flexibility at execution time relative to sequential plans. Current research on automated plan generation focuses on producing sequential plans, despite the appeal of POPs. In this paper we examine POP generation by relaxing or modifying the action orderings of a sequential plan to optimize for plan criteria that promote flexibility. Our approach relies on a novel partial weighted MaxSAT encoding of a sequential plan that supports the minimization of deordering or reordering of actions. Using a similar technique, we further demonstrate how to remove redundant actions from the plan, and how to combine this criterion with the objective of maximizing a POP's flexibility. Our partial weighted MaxSAT encoding allows us to compute a POP from a sequential plan effectively. We compare the efficiency of our approach to previous methods for POP generation via sequential-plan relaxation. Our results show that while an existing heuristic approach consistently produces the optimal deordering of a sequential plan, our approach has greater flexibility when we consider reordering the actions in the plan while also providing a guarantee of optimality. We also investigate and confirm the accuracy of the standard flex metric typically used to predict the true flexibility of a POP as measured by the number of linearizations it represents.

Intent Understanding Using an Activation Spreading Architecture

In this paper, we propose a new approach for recognizing intentions of humans by observing their activities with a color plus depth (RGB-D) camera. Activities and goals are modeled as a distributed network of inter-connected nodes in an Activation Spreading Network (ASN). Inspired by a formalism in hierarchical task networks, the structure of the network captures the hierarchical relationship between high-level goals and low-level activities that realize these goals. Our approach can detect intentions before they are realized and it can work in real-time. We also extend the formalism of ASNs to incorporate contextual information into intent recognition. We further augment the ASN formalism with special nodes and synaptic connections to model ordering constraints between actions, in order to represent and handle partial-order plans in our ASN. A fully functioning system is developed for experimental evaluation. We implemented a robotic system that uses our intent recognition to naturally interact with the user. Our ASN based intent recognizer is tested against three different scenarios involving everyday activities performed by a subject, and our results show that the proposed approach is able to detect low-level activities and recognize high-level intentions effectively in real-time. Further analysis shows that contextual and partial-order ASNs are able to discriminate between otherwise ambiguous goals.

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

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 temporalplanning framework, integrating recent techniques for solving temporalproblems under uncertainty. We discuss different ways of lifting the total order plans generated by the heuristic search to partial orderplans, showing (in)completeness results for each of them. We implemented our approach on top of COLIN, a state-of-the-art planner. An experimental evaluation over several benchmark problems shows the practical feasibility of the proposed approach.

FLAP: Applying least-commitment in forward-chaining planning

In this paper, we present FLAP, a partial-order planner that accurately applies the least-commitment principle that governs traditional partial-order planning. FLAP fully exploits the partial ordering among actions of a plan and hence it solves more problems than other similar approaches. The search engine of FLAP uses a combination of different state-based heuristics and applies a parallel search technique to diversify the search in different directions when a plateau is found. In the experimental evaluation, we compare FLAP with OPTIC, LPG-td and TFD, three state-of-the-art non-linear planners. The results show that FLAP outperforms these planners in terms of number of problems solved; in addition, the plans of FLAP represent a good trade-off between quality and computational time.

Distributed Heuristic Forward Search for Multi-agent Planning

This paper deals with the problem of classical planning for multiple cooperative agents who have private information about their local state and capabilities they do not want to reveal. Two main approaches have recently been proposed to solve this type of problem -- one is based on reduction to distributed constraint satisfaction, and the other on partial-order planning techniques. In classical single-agent planning, constraint-based and partial-order planning techniques are currently dominated by heuristic forward search. The question arises whether it is possible to formulate a distributed heuristic forward search algorithm for privacy-preserving classical multi-agent planning. Our work provides a positive answer to this question in the form of a general approach to distributed state-space search in which each agent performs only the part of the state expansion relevant to it. The resulting algorithms are simple and efficient -- outperforming previous algorithms by orders of magnitude -- while offering similar flexibility to that of forward-search algorithms for single-agent planning. Furthermore, one particular variant of our general approach yields a distributed version of the A* algorithm that is the first cost-optimal distributed algorithm for privacy-preserving planning.

FMAP: Distributed cooperative multi-agent planning

This paper proposes FMAP (Forward Multi-Agent Planning), a fully-distributed multi-agent planning method that integrates planning and coordination. Although FMAP is specifically aimed at solving problems that require cooperation among agents, the flexibility of the domain-independent planning model allows FMAP to tackle multi-agent planning tasks of any type. In FMAP, agents jointly explore the plan space by building up refinement plans through a complete and flexible forward-chaining partial-order planner. The search is guided by h D T G , a novel heuristic function that is based on the concepts of Domain Transition Graph and frontier state and is optimized to evaluate plans in distributed environments. Agents in FMAP apply an advanced privacy model that allows them to adequately keep private information while communicating only the data of the refinement plans that is relevant to each of the participating agents. Experimental results show that FMAP is a general-purpose approach that efficiently solves tightly-coupled domains that have specialized agents and cooperative goals as well as loosely-coupled problems. Specifically, the empirical evaluation shows that FMAP outperforms current MAP systems at solving complex planning tasks that are adapted from the International Planning Competition benchmarks.

The Complexity of Partial-Order Plan Viability Problems

Estimating the distance from a current partial-order plan to the goal state of the plan task is a challenging problem, with past research achieving only limited success. In an effort to understand the reasons for this situation, we investigate the computational complexity of the partial-order plan viability problem. We define several boundaries between the tractable and intractable subclasses of the problem, from which we identify several constraints that contribute to the computational intractability of the problem. These results bring new insights into the design and the development of future partial-order planning heuristics.