Partial Order Causal Link Research Articles

A unified constraint-based approach for plan and goal recognition from unreliable observations

Given a sequence of observations over a plan execution, plan and goal recognition are considered as interchangeable tasks in AI planning. However, strictly speaking, the former tries to identify a plan, and the latter a set of goals, that explain the observations. Both recognition tasks are data-driven, where data comprises the plan observations, and are specially useful in proactive systems. Depending on the source of knowledge about the agents under observation, these tasks are traditionally solved by two different approaches, which require a large plan library or a planning model. In between these approaches, we propose a unified novel constraint-based approach, which distinguishes between the two tasks but is valid for both. We present a formulation, based on Partial Order Causal Link planning, that is compiled from a small plan library, to approximate a model that learns the essential causality of the original planning model. We deal with unreliable observations, which include missing and noisy observations on the real world. Modeling the observations in our formulation is straightforward. The use of the learned model allows us to address a data-driven optimization task to find the plans that most satisfy those observations (plan recognition) and the goals that are sufficiently supported by the causal relationships of the observations (goal recognition). We perform a complete evaluation of our approach in IPC domains under several indicators (accuracy, spread and ROC curves) with varying degrees of partial observability and noise on the observations. We also perform a comparison with other model-based approaches from literature.

Hierarchical planning and reasoning about partially ordered plans—From theory to practice

AbstractThis invited paper (part of the New Faculty Highlights Invited Speaker Program of AAAI'21*) surveys my work done until today. The reviewed work focuses on hierarchical task network (HTN) planning as well as on partial order causal link (POCL) planning. Lines of research include theoretical investigations (mostly computational complexity analyses), heuristic search, as well as the practical application of the technology for planning‐based assistants, which support a human user in carrying out various tasks.

Hybrid Planning Heuristics Based on Task Decomposition Graphs

Hybrid Planning combines Hierarchical Task Network (HTN) planning with concepts known from Partial-Order Causal-Link (POCL) planning. We introduce novel heuristics for Hybrid Planning that estimate the number of necessary modifications to turn a partial plan into a solution. These estimates are based on the task decomposition graph that contains all decompositions of the abstract tasks in the planning domain. Our empirical evaluation shows that the proposed heuristics can significantly improve planning performance.

A Closer Look at Causal Links: Complexity Results for Delete-Relaxation in Partial Order Causal Link (POCL) Planning

Partial Order Causal Link (POCL) planning follows the principle of least commitment in that it maintains only a partial order on its actions to prevent unnecessary early commitment during search. This can reduce the search space significantly by systematically representing up to an exponential number of action sequences in just a single search node. Progress on goal achievement is represented fully by this partial order and by causal links, which represent the causal relationships between these actions as well as between the initial state and goal. Plan existence for a state in classical planning thus corresponds to plan existence for a partial plan in POCL planning. Yet almost no theoretical investigations for POCL plan existence were conducted so far. While delete-relaxation makes plan existence tractable in classical planning, we show it to be NP-hard in POCL planning unless the current plan is totally ordered or causal links are almost completely ignored.

POP ≡ POCL, Right? Complexity Results for Partial Order (Causal Link) Makespan Minimization

We study PO and POCL plans with regard to their makespan – the execution time when allowing the parallel execution of causally independent actions. Partially ordered (PO) plans are often assumed to be equivalent to partial order causal link (POCL) plans, where the causal relationships between actions are explicitly represented via causal links. As a first contribution, we study the similarities and differences of PO and POCL plans, thereby clarifying a common misconception about their relationship: There are PO plans for which there does not exist a POCL plan with the same orderings. We prove that we can still always find a POCL plan with the same makespan in polynomial time. As another main result we prove that turning a PO or POCL plan into one with minimal makespan by only removing ordering constraints (called deordering) is NP-complete. We provide a series of further results on special cases and implications, such as reordering, where orderings can be changed arbitrarily.

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.

A Computational Model of Plan-Based Narrative Conflict at the Fabula Level

Conflict is an essential element of interesting stories. In this paper, we operationalize a narratological definition of conflict and extend established narrative planning techniques to incorporate this definition. The conflict partial order causal link planning algorithm (CPOCL) allows narrative conflict to arise in a plan while maintaining causal soundness and character believability. We also define seven dimensions of conflict in terms of this algorithm's knowledge representation. The first three-participants, reason, and duration-are discrete values which answer the “who?” “why?” and “when?” questions, respectively. The last four-balance, directness, stakes, and resolution-are continuous values which describe important narrative properties that can be used to select conflicts based on the author's purpose. We also present the results of two empirical studies which validate our operationalizations of these narrative phenomena. Finally, we demonstrate the different kinds of stories which CPOCL can produce based on constraints on the seven dimensions.

CPOCL: A Narrative Planner Supporting Conflict

Conflict is an essential element of interesting stories, but little research in computer narrative has addressed it directly. We present a model of narrative conflict inspired by narratology research and based on Partial Order Causal Link (POCL) planning. This model informs an algorithm called CPOCL which extends previous research in story generation. Rather than eliminate all threatened causal links, CPOCL marks certain steps in a plan as non-executed in order to preserve the conflicting subplans of all characters without damaging the causal soundness of the overall story.

Story Planning: Creativity Through Exploration, Retrieval, and Analogical Transformation

Storytelling is a pervasive part of our daily lives and culture. The task of creating stories for the purposes of entertaining, educating, and training has traditionally been the purview of humans. This sets up the conditions for a creative authoring bottleneck where the consumption of stories outpaces the production of stories by human professional creators. The automation of story creation may scale up the ability to produce and deliver novel, meaningful story artifacts. From this practical perspective, story generation systems replicate the creative abilities of humans and can thus be considered instances of computational creativity. Computational systems that are purported to be creative typically utilize one of three general approaches: exploration of a space of concepts, combination of concepts, and transformation of concepts. In this article we present an approach to story generation that utilize exploration, combination, and transformation. Our approach, implemented in the Vignette Based Partial Order Causal Link story planner, is an algorithm that searches through a space of possible story solutions, guided by combinations of existing story fragments called vignettes. The vignettes are made relevant to novel story generation contexts through an automated transformation pre-process. Through these processes, we show that story generation can incorporate multiple perspectives on computational creativity. Our approach is presented at both the theoretical and technical levels.

Narrative Planning: Balancing Plot and Character

Narrative, and in particular storytelling, is an important part of the human experience. Consequently, computational systems that can reason about narrative can be more effective communicators, entertainers, educators, and trainers. One of the central challenges in computational narrative reasoning is narrative generation, the automated creation of meaningful event sequences. There are many factors -- logical and aesthetic -- that contribute to the success of a narrative artifact. Central to this success is its understandability. We argue that the following two attributes of narratives are universal: (a) the logical causal progression of plot, and (b) character believability. Character believability is the perception by the audience that the actions performed by characters do not negatively impact the audience's suspension of disbelief. Specifically, characters must be perceived by the audience to be intentional agents. In this article, we explore the use of refinement search as a technique for solving the narrative generation problem -- to find a sound and believable sequence of character actions that transforms an initial world state into a world state in which goal propositions hold. We describe a novel refinement search planning algorithm -- the Intent-based Partial Order Causal Link (IPOCL) planner -- that, in addition to creating causally sound plot progression, reasons about character intentionality by identifying possible character goals that explain their actions and creating plan structures that explain why those characters commit to their goals. We present the results of an empirical evaluation that demonstrates that narrative plans generated by the IPOCL algorithm support audience comprehension of character intentions better than plans generated by conventional partial-order planners.

Cinematic Visual Discourse: Representation, Generation, and Evaluation

In this paper, we present the design, implementation, and evaluation of an end-to-end camera planning system called Darshak. Darshak automatically constructs cinematic narrative discourse of a given story in a 3-D virtual environment. It utilizes a hierarchical partial-order causal link (POCL) planning algorithm to generate narrative plans that contain story events and camera directives for filming them. Dramatic situation patterns, commonly used by writers of fictional narratives, are formalized as communicative plan operators that provide a basis for structuring the cinematic content of the story's visualization. The dramatic patterns are realized through abstract communicative operators that represent operations on a viewer's beliefs about the story and its telling. Camera shot compositions and transitions are defined in this plan-based framework as execution primitives. Darshak's performance is evaluated through a novel user study based on techniques used to evaluate existing cognitive models of narrative comprehension. Initial study reveals significant effect of the choice of visualization strategies on measured viewer comprehension. It further shows significant effect of Darshak's choice of visualization strategy on comprehension.

Branching and pruning

A key feature of modern optimal planners such as graphplan and blackbox is their ability to prune large parts of the search space. Previous Partial Order Causal Link (POCL) planners provide an alte...

Branching and pruning: An optimal temporal POCL planner based on constraint programming

A key feature of modern optimal planners such as graphplan and blackbox is their ability to prune large parts of the search space. Previous Partial Order Causal Link (POCL) planners provide an alternative branching scheme but lacking comparable pruning mechanisms do not perform as well. In this paper, a domain-independent formulation of temporal planning based on Constraint Programming is introduced that successfully combines a POCL branching scheme with powerful and sound pruning rules. The key novelty in the formulation is the ability to reason about supports, precedences, and causal links involving actions that are not in the plan. Experiments over a wide range of benchmarks show that the resulting optimal temporal planner is much faster than current ones and is competitive with the best parallel planners in the special case in which actions have all the same duration. 1 1 This paper extends [V. Vidal, H. Geffner, Branching and pruning: An optimal temporal POCL planner based on constraint programming, in: Proceedings of AAAI-2004, San Jose, CA, 2004, pp. 570–577] by removing the canonicity restriction in the generation of plans. This is a restriction that forces every (ground) action in the domain to be done at most once in the plan. See the text for details.

VHPOP: Versatile Heuristic Partial Order Planner

VHPOP is a partial order causal link (POCL) planner loosely based on UCPOP. It draws from the experience gained in the early to mid 1990's on flaw selection strategies for POCL planning, and combines this with more recent developments in the field of domain independent planning such as distance based heuristics and reachability analysis. We present an adaptation of the additive heuristic for plan space planning, and modify it to account for possible reuse of existing actions in a plan. We also propose a large set of novel flaw selection strategies, and show how these can help us solve more problems than previously possible by POCL planners. VHPOP also supports planning with durative actions by incorporating standard techniques for temporal constraint reasoning. We demonstrate that the same heuristic techniques used to boost the performance of classical POCL planning can be effective in domains with durative actions as well. The result is a versatile heuristic POCL planner competitive with established CSP-based and heuristic state space planners.

Flaw Selection Strategies for Partial-Order Planning

Several recent studies have compared the relative efficiency of alternative flaw selection strategies for partial-order causal link (POCL) planning. We review this literature, and present new experimental results that generalize the earlier work and explain some of the discrepancies in it. In particular, we describe the Least-Cost Flaw Repair (LCFR) strategy developed and analyzed by Joslin and Pollack (1994), and compare it with other strategies, including Gerevini and Schubert's (1996) ZLIFO strategy. LCFR and ZLIFO make very different, and apparently conflicting claims about the most effective way to reduce search-space size in POCL planning. We resolve this conflict, arguing that much of the benefit that Gerevini and Schubert ascribe to the LIFO component of their ZLIFO strategy is better attributed to other causes. We show that for many problems, a strategy that combines least-cost flaw selection with the delay of separable threats will be effective in reducing search-space size, and will do so without excessive computational overhead. Although such a strategy thus provides a good default, we also show that certain domain characteristics may reduce its effectiveness.