In decision-theoretic planning problems, such as (partially observable) Markov decision problems or coordination graphs, agents typically aim to optimize a scalar value function. However, in many real-world problems agents are faced with multiple possibly conflicting objectives. In such multi-objective problems, the value is a vector rather than a scalar, and we need methods that compute a coverage set, i.e., a set of solutions optimal for all possible trade-offs between the objectives. In this project propose new multi-objective planning methods that compute the so-called convex coverage set (CCS): the coverage set for when policies can be stochastic, or the preferences are linear. We show that the CCS has favorable mathematical properties, and is typically much easier to compute that the Pareto front, which is often axiomatically assumed as the solution set for multi-objective decision problems.

Earth observation from space allows us to better understand natural phenomenas such as marine currents, to prevent or follow natural disasters, to follow climate evolution and many other things. To achieve that, there are a great number of artificial satellites orbiting Earth, equipped with high-resolution optical instruments and communicating with a network of ground stations. A satellite is said to be agile when it is able to move quickly around its gravity center along its three axes while moving along its orbit, thanks to gyroscopic actuators. It is equipped with a body-mounted optical instrument. To observe a ground area with the instrument, the satellite must be pointed to it. In practice, users submit observation requests to a mission center, which builds activity plans which are sent to the satellites. These plans contain several types of actions such as orbital maneuvers, acquisition realisations and acquisition downloads towards ground stations. Many techniques are used to synthesize such activity plans. Until now, plans are computed offline on the ground and converted into telecommands that the satellite executes strictly, without any flexibility. However, the satellite evolves in a dynamic environment. Unexpected events occur, such as meteorological changes or new urgent observation requests, that the system must handle. Moreover, resource consumption is not always well known. Until now, to ensure that plans will be executable on board with these uncertainties, they are built with worst-case hypothesis on resources consumption. The objective of this work is to give more autonomy to the satellite without compromising the predictability that is needed for some activities. On the ground, we have high computing power and high uncertainty, while on board we have very low computing power and low uncertainty. The main idea is to share decision-making between ground and board to take advantage of the high computing power on the ground and of the low uncertainty on board. First we apply this idea to download scheduling which consists in scheduling file downloads during ground station visibility windows. Second, we apply this idea to observation planning.

This model is used to automatically synthetise a controller that maps executions to the next action to perform. Many heuristics for cost-optimal planning are The problem is thus cast as a synthesis problem from a based on linear programming. We cover several given specification. Two obstacles for this approach are that interesting heuristics of this type by a common a suitable model for the task is needed, and that the synthesis framework that fixes the objective function of the problem is intractable in general. But, this intractability does linear program. Within the framework, constraints not preclude the approach from being effective in meaningful from different heuristics can be combined in one cases. Planning is the model-based approach to autonomous heuristic estimate which dominates the maximum behaviour.

Perhaps surprisingly, it is possible to predict how long an algorithm will take to run on a previously unseen input, using machine learning techniques to build a model of the algorithm's runtime as a function of problem-specific instance features. Such models have many important applications and over the past decade, a wide variety of techniques have been studied for building such models. In this extended abstract of our 2014 AI Journal article of the same title, we summarize existing models and describe new model families and various extensions. In a comprehensive empirical analyis using 11 algorithms and 35 instance distributions spanning a wide range of hard combinatorial problems, we demonstrate that our new models yield substantially better runtime predictions than previous approaches in terms of their generalization to new problem instances, to new algorithms from a parameterized space, and to both simultaneously.

We introduce a framework that enables an agent to autonomously learn its own symbolic representation of a low-level, continuous environment. Propositional symbols are formalized as names for probability distributions, providing a natural means of dealing with uncertain representations and probabilistic plans. We determine the symbols that are sufficient for computing the probability with which a plan will succeed, and demonstrate the acquisition of a symbolic representation in a computer game domain.

Planning with epistemic goals has received attention from both the dynamic logic and planning communities. In the single-agent case, under the epistemic closed-world assumption (ECWA), epistemic planning can be reduced to contingent planning. However, it is inappropriate to make the ECWA in some epistemic planning scenarios, for example, when the agent is not fully introspective, or when the agent wants to devise a generic plan that applies to a wide range of situations. In this paper, we propose a complete single-agent epistemic planner without the ECWA. We identify two normal forms of epistemic formulas: weak minimal epistemic DNF and weak minimal epistemic CNF, and present the progression and entailment algorithms based on these normal forms. We adapt the PrAO algorithm for contingent planning from the literature as the main planning algorithm and develop a complete epistemic planner called EPK. Our experimental results show that EPK can generate solutions effectively for most of the epistemic planning problems we have considered including those without the ECWA.

Whereas previous work on non-deterministic planning has focused on characterizing (and computing) "loopy" but "closed" plans, we look here at the kind of environments that these plans are to be executed in. In particular, we provide a logical characterization of the standard "fairness'' assumption used, and show that strong cyclic plans are correct solution concepts for fair environments.  We argue then that such logical characterization allows us to recast non-deterministic planning as a reactive synthesis task, and show that for a special case, recent efficient synthesis techniques can be applied.

We address a spatial conservation planning problem in which the planner purchases a budget-constrained set of land parcels in order to maximize the expected spread of a population of an endangered species. Existing techniques based on the sample average approximation scheme and standard integer programming methods have high complexity and limited scalability. We propose a fast combinatorial optimization algorithm using Lagrangian relaxation and primal-dual techniques to solve the problem approximately. The algorithm provides a new way to address a range of conservation planning and scheduling problems. On the Red-cockaded Woodpecker data, our algorithm produces near optimal solutions and runs significantly faster than a standard mixed integer program solver. Compared with a greedy baseline, the solution quality is comparable or better, but our algorithm is 10–30 times faster. On synthetic problems that do not exhibit submodularity, our algorithm significantly outperforms the greedy baseline.

Goal-driven autonomy (GDA) agents reason about goals while introspectively examining if their course of action matches their expectations. Many GDA agents adopt a hierarchical planning model to generate plans but limit reasoning with expectations to individual actions or projecting the expected state. In this paper we present a relaxation of this limitation. Taking advantage of hierarchical planning principles, our GDA agent elicits expectations that not only validate the next action but the overall plan trajectory without requiring validation against the complete state. We report on (1) a formalization of GDA's expectations that covers trajectories, (2) an implementation of these ideas and (3) benchmarking on two domains used in the GDA literature.

Current motion planners, such as the ones available in ROS MoveIt, can solve difficult motion planning problems. However, these planners are not practical in unstructured, rapidly-changing environments. First, they assume that the environment is well-known, and static during planning and execution. Second, they do not support temporal constraints, which are often important for synchronization between a robot and other actors. Third, because many popular planners generate completely new trajectories for each planning problem, they do not allow for representing persistent control policy information associated with a trajectory across planning problems. We present Chekhov, a reactive, integrated motion planning and execution system that addresses these problems. Chekhov uses a Tube-based Roadmap in which the edges of the roadmap graph are families of trajectories called flow tubes, rather than the single trajectories commonly used in roadmap systems. Flow tubes contain control policy information about how to move through the tube, and also represent the dynamic limits of the system, which imply temporal constraints. This, combined with an incremental APSP algorithm for quickly finding paths in the roadmap graph, allows Chekhov to operate in rapidly changing environments. Testing in simulation, and with a robot testbed has shown improvement in planning speed and motion predictability over current motion planners.