Lazy Evaluation of Negative Preconditions in Planning Domains (Extended Abstract)

AI planning technology faces performance issues with large-scale problems with negative preconditions. In this extended abstract, we show how to leverage the power of the Finite Domain Representation (FDR) used by the popular Fast Downward planner for such domains. FDR improves scalability thanks to its use of multi-valued state variables. However, it scales poorly when dealing with negative preconditions. We propose an alternative hybrid approach that evaluates negative preconditions on the fly during search but only when strictly needed. This is compared to the traditional use of PDDL bookmark predicates, which increases memory usage

SMT-based Abstract Temporal Planning

These are the proceedings of the International Workshop on Petri Nets and Software Engineering (PNSE’14) in Tunis, Tunisia, June 23–24, 2014. It is a co-located event of Petri Nets 2014, the 35th international conference on Applications and Theory of Petri Nets and Concurrency and ACSD 2014, the 14th International Conference on Application of Concurrency to System Design.An abstract planning is the first phase of the web service composition in the PlanICS framework. A user query specifies the initial and the expected state of a plan in request. The paper extends PlanICS with a module for temporal planning, by extending the user query with an LTL_k-X formula specifying temporal aspects of world transformations in a plan. Our solution comes together with an example, an implementation, and experimental results

ECHO: A hierarchical combination of classical and multi-agent epistemic planning problems

The continuous interest in Artificial Intelligence (AI) has brought, among other things, the development of several scenarios where multiple artificial entities interact with each other. As for all the other autonomous settings, these multi-agent systems require orchestration. This is, generally, achieved through techniques derived from the vast field of Automated Planning. Notably, arbitration in multi-agent domains is not only tasked with regulating how the agents act, but must also consider the interactions between the agents' information flows and must, therefore, reason on an epistemic level. This brings a substantial overhead that often diminishes the reasoning process's usability in real-world situations. To address this problem, we present ECHO, a hierarchical framework that embeds classical and multi-agent epistemic (epistemic, for brevity) planners in a single architecture. The idea is to combine (i) classical; and(ii) epistemic solvers to model efficiently the agents' interactions with the (i) 'physical world'; and(ii) information flows, respectively. In particular, the presented architecture starts by planning on the 'epistemic level', with a high level of abstraction, focusing only on the information flows. Then it refines the planning process, due to the classical planner, to fully characterize the interactions with the 'physical' world. To further optimize the solving process, we introduced the concept of macros in epistemic planning and enriched the 'classical' part of the domain with goal-networks. Finally, we evaluated our approach in an actual robotic environment showing that our architecture indeed reduces the overall computational time

Development of perception module for bobotic manipulation tasks

Robots performing manipulation tasks require the accurate location and orientation of an object in space. Previously, at the Robotics Laboratory of IOC-UPC this data has been generated artificially. In order to automate the process, a perception module has been developed for providing task and motion planners with the localization and pose estimation of objects used in robot manipulation tasks. The Robot Operating System provided a great framework for incorporating vision provided by Microsoft Kinect V2 sensors and the presentation of obtained data to be used in the generation of Planning Domain Definition Language files, which define a robots environment. Localization and pose estimation was done using fiducial markers along with studying possible enhancements using deep learning methods. Perfectly calibrating hardware and setting up a system play a big role in enhancing perception accuracy and while fiducial markers provide a simple and robust solution in laboratory conditions, real world applications with varying lighting, viewing angles and partial occlusions should rely on AI visio

Integrating Recognition and Decision Making to Close the Interaction Loop for Autonomous Systems

Intelligent systems are becoming increasingly ubiquitous in daily life. Mobile devices are providing machine-generated support to users, robots are coming out of their cages in manufacturing to interact with co-workers, and cars with various degrees of self-driving capabilities operate amongst pedestrians and the driver. However, these interactive intelligent systems\u27 effectiveness depends on their understanding and recognition of human activities and goals, as well as their responses to people in a timely manner. The average person does not follow instructions step-by-step or act in a formulaic manner, but instead varies the order of actions and timing when performing a given task. People explore their surroundings, make mistakes, and may interrupt an activity to handle more urgent matters. The decisions that an autonomous intelligent system makes should account for such noise and variance regardless of the form of interaction, which includes adapting action choices and possibly its own goals.While most people take these aspects of interaction for granted, they are complex and involve many specific tasks that have primarily been studied independently within artificial intelligence. This results in open-loop interactive experiences where the user must perform a fixed input command or the intelligent system performs a hard-coded output response---one of the components of the interaction cannot adapt with respect to the other for longer-term back-and-forth interactions. This dissertation explores how developments in plan recognition, activity recognition, intent recognition, and autonomous planning can work together to develop more adaptive interactive experiences between autonomous intelligent systems and the people around them. In particular, we consider a unifying perspective of recognition algorithms that provides sufficient information to dynamically produce short-term automated planning problems, and we present ways to run these algorithms faster for the real-time needs of interaction. This exploration leads to the introduction of the Planning and Recognition Together Close the Interaction Loop (PReTCIL) framework that serves as a first step towards identifying how we can address the problem of closing the interaction loop, in addition to new questions that need to be considered

Programmation par contraintes sur les flux de données

We study the generalization of constraint programming on variables finite domains with variable flow. On the one hand, the flow of concepts, infinite sequences and infinite words have been the subject of numerous studies, and a goal is to achieve a state of the art covering language theory, classical and temporal logics as well as many related formalisms. The reconciliation performed with temporal logics is a first step towards unification formalisms on flows and temporal logics being themselves many, we establish a classification of these will allow the extrapolation of contributions to other contexts. The second objective is to identify the elements of these formalisms that allow the processing of satisfaction problems with the techniques of constraint programming on finite domain variables. Compared to the expressiveness of temporal logic, that of our formalism is more limited. This is due to the fact that constraint programming allows only the conjunction of constraints and requires integrating the disjunction in the notion of constraint propagator. Our formalism allows a gain in conciseness and reuse of the concept of propagator. The issue of generalization to more expressive logics is left open.Nous étudions la généralisation de la programmation par contraintes sur les variables à domaines finis aux variables flux. D'une part, les concepts de flux, de séquences infinies et de mots infinis ont fait l'objet de nombreux travaux, et un objectif consiste à réaliser un état de l'art qui couvre la théorie des langages, les logiques classiques et temporelles, ainsi que les nombreux formalismes apparentés. Le rapprochement effectué avec les logiques temporelles est un premier pas vers l'unification des formalismes sur les flux, et les logiques temporelles étant elles-même nombreuses, nous établissons une classification de celles-ci qui permettra l'extrapolation des contributions à d'autres contextes. Le second objectif consiste à identifier les éléments de ces formalismes qui permettent le traitement des problèmes de satisfaction avec les techniques de la programmation par contraintes sur les variables à domaines finis. Comparée à l'expressivité des logiques temporelles, celle de notre formalisme est plus limitée. Ceci est dû au fait que la programmation par contraintes ne permet que la conjonction de contraintes, et impose d'intégrer la disjonction dans la notion de propagateur de contraintes. Notre formalisme permet un gain en concision et la réutilisation de la notion de propagateur. La question de la généralisation à des logiques plus expressives est laissée ouverte

Beyond the Frontiers of Timeline-based Planning

Any agent, either biological or artificial, understands how to behave in its environment according to its prior knowledge and to its prior experience. The process of deciding which actions to undertake and how to perform them so as to achieve some desired objective is called deliberation. In particular, planning is an abstract and explicit deliberation process that chooses and organizes actions, by anticipating their expected outcomes, with the aim to achieve, as best as possible, some pre-stated objectives called goals. Among the most widespread approaches to automated planning, the classical approach broadly pursues to the following definition of planning: starting from a description of the initial state of the world, a description of the desired goals, and a description of a set of possible actions, the planning problem consists in synthesizing a plan, i.e., a sequence of actions, that is guaranteed, when applied to the initial state, to generate a state, called a goal state, which contains the desired goals. In order to cope with computational complexity, however, the classical approach to planning introduces some restrictive assumptions. Among them, for example, there is no explicit model of time and concurrency is treated only roughly. Additionally, goals are specified as a set of goal states, therefore, objectives such as states to be avoided and constraints on state trajectories or utility functions are not handled. In order to relax these restrictions, some alternative approaches have been proposed over the years. The timeline-based approach to planning, in particular, represents an effective alternative to classical planning for complex domains requiring the use of both temporal reasoning and scheduling features. This thesis focuses on timeline-based planning, aiming at solving some efficiency issues which inevitably raise as a consequence of the drop out of these restrictions. Regardless of the followed approach, indeed, it turns out that automated planning is a rather complex task from a computational point of view. Furthermore, not all of the approaches proposed in literature can rely on effective heuristics for efficiently tackling the search. This is particularly true in the case of the more recent and hence less investigated timeline-based formulation. Most of the timeline-based planners, in particular, have usually neglected the advantages triggered in classical planning from the use of Graphplan and/or modern heuristic search, namely the capability of reasoning on the whole domain model. This thesis aims at reducing the performance gap between the classical approach at planning and the timeline-based one. Specifically, the overall goal is to improve the efficiency of timeline-based reasoners taking inspiration from techniques applied in more classical approaches to planning. The main contributions of this thesis, therefore, are a) a new formalism for timeline-based planning which overcomes some limitations of the existing ones; b) a set of heuristics, inspired by the classical approach, that improve the performance of the timeline-based approach to planning; c) the introduction of sophisticated techniques like the non-chronological backtracking and the no-good learning, commonly used in other fields such as Constraint Processing, into the search process;d) the reorganization of the existing solver architectures, of a new solver called ORATIO, that allows to push the reasoning process beyond the sole automated planning, winking at emerging fields like, for example, Explainable AI and e) the introduction of a new language for expressing timeline-based planning problems called RIDDLE