Progress in AI Planning Research and Applications

Planning has made significant progress since its inception in the 1970s, in terms both of the efficiency and sophistication of its algorithms and representations and its potential for application to real problems. In this paper we sketch the foundations of planning as a sub-field of Artificial Intelligence and the history of its development over the past three decades. Then some of the recent achievements within the field are discussed and provided some experimental data demonstrating the progress that has been made in the application of general planners to realistic and complex problems. The paper concludes by identifying some of the open issues that remain as important challenges for future research in planning

Temporal and Hierarchical Models for Planning and Acting in Robotics

The field of AI planning has seen rapid progress over the last decade and planners are now able to find plan with hundreds of actions in a matter of seconds. Despite those important progresses, robotic systems still tend to have a reactive architecture with very little deliberation on the course of the plan they might follow. In this thesis, we argue that a successful integration with a robotic system requires the planner to have capacities for both temporal and hierarchical reasoning. The former is indeed a universal resource central in many robot activities while the latter is a critical component for the integration of reasoning capabilities at different abstraction levels, typically starting with a high level view of an activity that is iteratively refined down to motion primitives. As a first step to carry out this vision, we present a model for temporal planning unifying the generative and hierarchical approaches. At the center of the model are temporal action templates, similar to those of PDDL complemented with a specification of the initial state as well as the expected evolution of the environment over time. In addition, our model allows for the specification of hierarchical knowledge possibly with a partial coverage. Consequently, our model generalizes the existing generative and HTN approaches together with an explicit time representation. In the second chapter, we introduce a planning procedure suitable for our planning model. In order to support hierarchical features, we extend the existing Partial-Order Causal Link approach used in many constraintbased planners, with the notions of task and decomposition. We implement it in FAPE (Flexible Acting and Planning Environment) together with automated problem analysis techniques used for search guidance. We show FAPE to have performance similar to state of the art temporal planners when used in a generative setting. The addition of hierarchical information leads to further performance gain and allows us to outperform traditional planners. In the third chapter, we study the usual methods used to reason on temporal uncertainty while planning. We relax the usual assumption of total observability and instead provide techniques to reason on the observations needed to maintain a plan dispatchable. We show how such needed observations can be detected at planning time and incrementally dealt with by considering the appropriate sensing actions. In a final chapter, we discuss the place of the proposed planning system as a central component for the control of a robotic actor. We demonstrate how the explicit time representation facilitates plan monitoring and action dispatching when dealing with contingent events that require observation. We take advantage of the constraint-based and hierarchical representation to facilitate both plan-repair procedures as well opportunistic plan refinement at acting time

Combined heuristic task and motion planning for bi-manual robots

Planning efficiently at task and motion levels allows the setting of new challenges for robotic manipulation problems, like for instance constrained table-top problems for bi-manual robots. In this scope, the appropriate combination of task and motion planning levels plays an important role. Accordingly, a heuristic-based task and motion planning approach is proposed, in which the computation of the heuristic addresses a geometrically relaxed problem, i.e., it only reasons upon objects placements, grasp poses, and inverse kinematics solutions. Motion paths are evaluated lazily, i.e., only after an action has been selected by the heuristic. This reduces the number of calls to the motion planner, while backtracking is reduced because the heuristic captures most of the geometric constraints. The approach has been validated in simulation and on a real robot, with different classes of table-top manipulation problems. Empirical comparison with recent approaches solving similar problems is also reported, showing that the proposed approach results in significant improvement both in terms of planing time and success rate.Peer ReviewedPostprint (author's final draft

Verification and Validation of Planning Domain Models

The verification and validation of planning domain models is one of the biggest challenges to deploying planning-based automated systems in the real world.The state-of-the-art verification methods of planning domain models are vulnerable to false positives, i.e. counterexamples that are unreachable by sound planners when using the domain under verification during planning tasks. False positives mislead designers into believing correct models are faulty. Consequently, designers needlessly debug correct models to remove these false positives. This process might unnecessarily constrain planning domain models, which can eradicate valid and sometimes required behaviours. Moreover, catching and debugging errors without knowing they are false positives can give verification engineers a false sense of achievement, which might cause them to overlook valid errors.To address this shortfall, the first part of this thesis introduces goal-constrained planning domain model verification, a novel approach that constrains the verification of planning domain models with planning goals to reduce the number of unreachable planning counterexamples. This thesis formally proves the correctness of this method and demonstrates the application of this approach using the model checker Spin and the planner MIPS-XXL. Furthermore, it reports the empirical experiments that validate the feasibility and investigates the performance of the goal-constrained verification approach. The experiments show that not only the goal-constrained verification method is robust against false positive errors, but it also outperforms under-constrained verification tasks in terms of time and memory in some cases.The second part of this thesis investigates the problem of validating the functional equivalence of planning domain models. The need for techniques to validate the functional equivalence of planning domain models has been highlighted in previous research and has applications in model learning, development and extension. Despite the need and importance of proving the functional equivalence of planning domain models, this problem attracted limited research interest.This thesis builds on and extends previous research by proposing a novel approach to validate the functional equivalence of planning domain models. First, this approach employs a planner to remove redundant operators from the given domain models; then, it uses a Satisfiability Modulo Theories (SMT) solver to check if a predicate mapping exists between the two domain models that makes them functionally equivalent. The soundness and completeness of this functional equivalence validation method are formally proven in this thesis.Furthermore, this thesis introduces D-VAL, the first planning domain model automatic validation tool. D-VAL uses the FF planner and the Z3 SMT solver to prove the functional equivalence of planning domain models. Moreover, this thesis demonstrates the feasibility and evaluates the performance of D-VAL against thirteen planning domain models from the International Planning Competition (IPC). Empirical evaluation shows that D-VAL validates the functional equivalence of the most challenging task in less than 43 seconds. These experiments and their results provide a benchmark to evaluate the feasibility and performance of future related work

Combining task and motion planning for mobile manipulators

Aplicat embargament des de la data de defensa fins el dia 31/12/2019Premi Extraordinari de Doctorat, promoció 2018-2019. Àmbit d’Enginyeria IndustrialThis thesis addresses the combination of task and motion planning which deals with different types of robotic manipulation problems. Manipulation problems are referred to as mobile manipulation, collaborative multiple mobile robots tasks, and even higher dimensional tasks (like bi-manual robots or mobile manipulators). Task and motion planning problems needs to obtain a geometrically feasible manipulation plan through symbolic and geometric search space. The combination of task and motion planning levels has emerged as a challenging issue as the failure leads robots to dead-end tasks due to geometric constraints. In addition, task planning is combined with physics-based motion planning and information to cope with manipulation tasks in which interactions between robots and objects are required, or also a low-cost feasible plan in terms of power is looked for. Moreover, combining task and motion planning frameworks is enriched by introducing manipulation knowledge. It facilitates the planning process and aids to provide the way of executing symbolic actions. Combining task and motion planning can be considered under uncertain information and with human-interaction. Uncertainty can be viewed in the initial state of the robot world or the result of symbolic actions. To deal with such issues, contingent-based task and motion planning is proposed using a perception system and human knowledge. Also, robots can ask human for those tasks which are difficult or infeasible for the purpose of collaboration. An implementation framework to combine different types of task and motion planning is presented. All the required modules and tools are also illustrated. As some task planning algorithms are implemented in Prolog or C++ languages and our geometric reasoner is developed in C++, the flow of information between different languages is explained.Aquesta tesis es centra en les eines de planificació combinada a nivell de tasca i a nivell de moviments per abordar diferents problemes de manipulació robòtica. Els problemes considerats són de navegació de robots mòbil enmig de obstacles no fixes, tasques de manipulació cooperativa entre varis robots mòbils, i tasques de manipulació de dimensió més elevada com les portades a terme amb robots bi-braç o manipuladors mòbils. La planificació combinada de tasques i de moviments ha de cercar un pla de manipulació que sigui geomètricament realitzable, a través de d'un espai de cerca simbòlic i geomètric. La combinació dels nivells de planificació de tasca i de moviments ha sorgit com un repte ja que les fallades degudes a les restriccions geomètriques poden portar a tasques sense solució. Addicionalment, la planificació a nivell de tasca es combina amb informació de la física de l'entorn i amb mètodes de planificació basats en la física, per abordar tasques de manipulació en les que la interacció entre el robot i els objectes és necessària, o també si es busca un pla realitzable i amb un baix cost en termes de potència. A més, el marc proposat per al combinació de la planificació a nivell de tasca i a nivell de moviments es millora mitjançant l'ús de coneixement, que facilita el procés de planificació i ajuda a trobar la forma d'executar accions simbòliques. La combinació de nivells de planificació també es pot considerar en casos d'informació incompleta i en la interacció humà-robot. La incertesa es considera en l'estat inicial i en el resultat de les accions simbòliques. Per abordar aquest problema, es proposa la planificació basada en contingències usant un sistema de percepció i el coneixement de l'operari humà. Igualment, els robots poden demanar col·laboració a l'operari humà per a que realitzi aquelles accions que són difícils o no realitzables pel robot. Es presenta també un marc d'implementació per a la combinació de nivells de planificació usant diferents mètodes, incloent tots els mòduls i eines necessàries. Com que alguns algorismes estan implementats en Prolog i d'altres en C++, i el mòdul de raonament geomètric proposat està desenvolupat en C++, es detalla el flux d'informació entre diferents llenguatges.Award-winningPostprint (published version

Modelling and Analysis of Network Security Policies

Nowadays, computers and network communications have a pervasive presence in all our daily activities. Their correct configuration in terms of security is becoming more and more complex due to the growing number and variety of services present in a network. Generally, the security configuration of a computer network is dictated by specifying the policies of the security controls (e.g. firewall, VPN gateway) in the network. This implies that the specification of the network security policies is a crucial step to avoid errors in network configuration (e.g., blocking legitimate traffic, permitting unwanted traffic or sending insecure data). In the literature, an anomaly is an incorrect policy specification that an administrator may introduce in the network. In this thesis, we indicate as policy anomaly any conflict (e.g. two triggered policy rules enforcing contradictory actions), error (e.g. a policy cannot be enforced because it requires a cryptographic algorithm not supported by the security controls) or sub-optimization (e.g. redundant policies) that may arise in the policy specification phase. Security administrators, thus, have to face the hard job of correctly specifying the policies, which requires a high level of competence. Several studies have confirmed, in fact, that many security breaches and breakdowns are attributable to administrators’ responsibilities. Several approaches have been proposed to analyze the presence of anomalies among policy rules, in order to enforce a correct security configuration. However, we have identified two limitations of such approaches. On one hand, current literature identifies only the anomalies among policies of a single security technology (i.e., IPsec, TLS), while a network is generally configured with many technologies. On the other hand, existing approaches work on a single policy type, also named domain (i.e., filtering, communication protection). Unfortunately, the complexity of real systems is not self-contained and each network security control may affect the behavior of other controls in the same network. The objective of this PhD work was to investigate novel approaches for modelling security policies and their anomalies, and formal techniques of anomaly analysis. We present in this dissertation our contributions to the current policy analysis state of the art and the achieved results. A first contribution was the definition of a new class of policy anomalies, i.e. the inter-technology anomalies, which arises in a set of policies of multiple security technologies. We provided also a formal model able to detect these new types of anomalies. One of the results achieved by applying the inter-technology analysis to the communication protection policies was to categorize twelve new types of anomalies. The second result of this activity was derived from an empirical assessment that proved the practical significance of detecting such new anomalies. The second contribution of this thesis was the definition of a newly-defined type of policy analysis, named inter-domain analysis, which identifies any anomaly that may arise among different policy domains. We improved the state of the art by proposing a possible model to detect the inter-domain anomalies, which is a generalization of the aforementioned inter-technology model. In particular, we defined the Unified Model for Policy Analysis (UMPA) to perform the inter-domain analysis by extending the analysis model applied for a single policy domain to comprehensive analysis of anomalies among many policy domains. The result of this last part of our dissertation was to improve the effectiveness of the analysis process. Thanks to the inter-domain analysis, indeed, administrators can detect in a simple and customizable way a greater set of anomalies than the sets they could detect by running individually any other model