In this study, we propose a method for learning interactions among different types of objects to devise new plans using these objects. Learning is accomplished by observing a given sequence of events with their timestamps and using spatial information on the initial state of the objects in the environment. We assume that no intermediate state information is available about the states of objects. We have used the Incredible Machine game as a suitable domain for analyzing and learning object interactions. When a knowledge base about relations among objects is provided, interactions to devise new plans are learned to a desired extent. Moreover, using spatial information of objects or temporal information of events makes it feasible to learn the conditional effects of objects on each other. Our analyses show that, integrating spatial and temporal data in a spatio-temporal learning approach gives closer results to that of the knowledge-based approach by providing applicable event models for planning. This is promising because gathering spatio-temporal information does not require great amount of knowledge.

This paper presents a prototype system called SFplanner which uses an automated planning technique to generate workflows for reconfiguring a computing infrastructure. The system allows an administrator to specify a configuration task which consists of current state, desired state and global constraints. This task is compiled to a grounded finite-domain representation as the input for the standard (unmodified) Fast-Downward planner in order to automatically generate a workflow. The execution of the workflow will bring the system into the desired state, preserving the global constraints at every stage of the workflow.

Classical planning has made huge advances in the last twenty years, leading to solvers able to create plans with thousands of actions for problems described by hundreds of propositions. Yet, the assumptions of classical planning (determinism, model completeness, etc) are oen criticised as being too restrictive to address "real" planning problems. Recently, however, many researchers have started to exploit the good performance of classical planners, through compilations and other methods of reuse, to solve a much wider range of problems. In this way, classical planners have been used for dealing with more expressive planning problems, including incomplete information, temporally extended goals and preferences, as well as to solve problems in various areas of application. Likewise, approaches range from pure compilation (translating a problem into PDDL and solving it with a classical planner) to embedding classical planning techniques inside dedicated algorithms. is body of contributions help illustrate how the results of decades of research in classical planning are now being put to use.

This paper documents extending the ELEXIR (Engine for LEXicalized Intent Recognition) system (Geib 2009; Geib 2011) with a world model. This is a significant increase in the expressiveness of the plan recognition system and allows a number of additions to the algorithm, most significantly conditioning probabilities for recognized plans on the state of the world during execution. Since, ELEXIR falls in the family of gramatical methods for plan recognition in viewing the problem of plan recognition as that of parsing, this paper will also briefly discuss how this extension relates to state of the art proposals in the natural language community regarding probabilistic parsing.

We present the domain-independent HRFF algorithm, which solves goal-oriented HMDPs by incrementally aggregating plans generated by the Metric-FF planner into a policy defined over discrete and continuous state variables. HRFF takes into account non-monotonic state variables, and complex combinations of many discrete and continuous probability distributions. We introduce new data structures and algorithmic paradigms to deal with continuous state spaces: hybrid hierarchical hash tables, domain determinization based on dynamic domain sampling or on static computation of probability distributions' modes, optimization settings under Metric-FF based on plan probability and length. We compare with HAO* on the Rover domain and show that HRFF outperforms HAO* by many order of magnitudes in terms of computation time and memory usage. We also experiment challenging and combinatorial HMDP versions of benchmarks from numeric classical planning, with continuous dead-ends and non-monotonic continuous state variables.

Most of the research in Automated Planning relies on the evaluation of different techniques over a set of benchmarks. The generation of planning tasks for these benchmarks is done using generators coded ad-hoc. Instead, we propose an approach for generating planning problems automatically given the domain definition and some declarative semantics-related information provided by the user. The approach consists of modelling the task of generating planning problems also as a planning problem. The main contribution of this work is that the generation of planning problems is partially handled in a domain-independent way, which leads to a saving of time and effort for researchers. Additionally, the declarative input to the generator facilitates the modification of its behavior. This is a feature of interest for generating different problem distributions.

Motivated by the success of the translation-based approach for conformant planning, introduced by Palacios and Geffner,  we present two variants of a new compilation scheme from conformant probabilistic planning problems (CPP) to variants of classicalplanning.In CPP, we are given a set of actions -- which we assume to be deterministic in this paper, a distribution over initial states, a goal condition, and a value $0<p\leq 1$. Our task is to find a plan $\pi$ such that the goal probability following the execution of $\pi$ in the initial state is at least $p$. Our firstvariant translates CPP into classicalplanning with resource constraints, in which the resource represents probabilities of failure.  The second variant translates CPPinto cost-optimal classical planning problems, in which costs represents probabilities. Empirically, these techniques show mixed results, performing very well on some domains, and poorly on others. This  indicates that compilation-based technique are a feasible and promising direction for solving CPP problems and, possibly, more general probabilistic planning problems.

Recently, a new language AC(C) was proposed to integrate answer set programming (ASP) and constraint logic programming (CLP). In this paper, we show that temporally expressive planning problems in PDDL2.1 can be translated into AC(C) and solved using AC(C) solvers. Compared with existing approaches, the new approach puts less restrictions on the planning problems and is easy to extend with new features like PDDL axioms. It can also leverage the inference engine for AC(C) which has the potential to exploit the best reasoning mechanisms developed in the ASP, SAT and CP communities.

The cost of an optimal delete relaxed plan, known as h+, is a powerful admissible heuristic but is in general intractable to compute. In this paper we examine the problem of computing h+ by encoding it as a MAXSAT problem. We develop a new encoding that utilizes constraint generation to support the computation of a sequence of increasing lower bounds on h+. We show a close connection between the computations performed by a recent approach for solving MAXSAT and a hitting set approach recently proposed for computing h+. Using this connection we observe that our MAXSAT computation can be initialized with a set of landmarks computed by LM-cut. By judicious use of MAXSAT solving along with a technique of lazy heuristic evaluation we obtain speedups for finding optimal plans over LM-cut on a number of domains. Our approach enables the exploitation of continued progress in MAXSAT solving, and also makes it possible to consider computing or approximating heuristics that are even more informed that h+ by, for example, adding some information about deletes back into the encoding.

Recent work on planning in incomplete domains focuses on constructing plans that succeed despite incomplete knowledge of action preconditions and effects. As planning models become more expressive, such as in temporal planning, the types of incompleteness may not only change, but plans become more challenging to evaluate. The primary difficulty to temporal plan evaluation is accounting for temporal constraints that may not be satisfied under all interpretations of the incomplete domain. In this work, we formulate incomplete temporal plan evaluation as a generalization of the temporal consistency problem, called partial temporal consistency. We present a knowledge compilation approach that is combined with symbolic constraint propagation and model counting algorithms for counting the number of incomplete domain model interpretations under which a plan is consistent. We present an evaluation that identifies the aspects of incomplete temporal plans most impact performance.