This paper introduces a counter-example based approach for solving probabilistic conformant planning (PCP) problems. Our algorithm incrementally generates candidate plans and identifies counter-examples until it finds a plan for which the probability of success is above the specified threshold. We prove that the algorithm is sound and complete. We further propose a variation of our algorithm that uses hitting sets to accelerate the generation of candidate plans. Experimental results show that our planner is particularly suited for problems with a high probability threshold.

We demonstrate Dagster, a system that implements a new approach to scheduling interdependent (Boolean) SAT search activities in high-performance computing (HPC) environments. Our system takes as input a set of disjunctive clauses (i.e., DIMACS CNF) and a labelled directed acyclic graph (DAG) structure describing how the clauses are decomposed into a set of interrelated problems. Component problems are solved using standard systematic backtracking search, which may optionally be coupled to (stochastic dynamic) local search and/or clause-strengthening processes. We demonstrate Dagster using a new Graph Maximal Determinant combinatorial case study. This demonstration paper presents a new case study, and is adjunct to the longer accepted manuscript at the Pacific Rim International Conference on Artificial Intelligence (2022).

Property Directed Reachability (PDR) is a relatively new SAT-based search paradigm for classical AI planning. Compared to earlier SAT-based paradigms, PDR proceeds without unrolling the system transition function, and therefore without having the underlying procedure reason about potentially computationally expensive multi-step formulae. By maintaining a queue of obligations - i.e., a state at a timestep - and knowledge about what is possible at each planning step, PDR iteratively evaluates whether an obligation can be progressed by one step towards the goal. We develop and evaluate two new distributed PDR algorithms for planning, and additionally implement serial and portfolio PDR algorithms for planning. We are the first to consider distributed PDR for planning and the first to consider PDR based on incremental SAT solving in that setting. Our first new algorithm, PS-PDR, evaluates many obligations independently in parallel using a pool of incremental SAT workers. PS-PDR is unique amongst distributed PDR algorithms in centrally maintaining a single queue of obligations, enabling an efficient focused search compared to a PDR portfolio. Our second new algorithm, PD-PDR, sequences subproblems according to the compositional structure of the concrete problem at hand. Subproblems are solved independently in parallel, with a concrete plan obtained by combining subproblem plans. Our experimental evaluation exhibits strong runtime gains for both new algorithms in both satisfiable and unsatisfiable planning benchmarks.

We aim to significantly enhance the science return of astronomical observatories, and in particular giant terrestrial optical telescopes. Observatories employ Adaptive Optics (AO) systems in order to acquire high sensitivity diffraction limited images of the sky. The incumbent “workhorse” for control of AO systems employs a linear real-time controller in a closed loop, with sensing of state performed via a (Shack-Hartmann) wavefront sensor (WFS). The actuators of a deformable mirror (DM) are driven, with the action performed in each iteration having a continuous representation as an array of DC voltages. The typical control regime is practical and scalable, nonetheless, there remains a residual uncompensated turbulence that leads to optical aberrations limiting the class of scientific assets that can be acquired. We have developed and trained a translational GAN model that accurately estimates residual perturbations from WFS images. Model inference occurs in 0.34 milliseconds using off-the-shelf GPU hardware, and is applicable for use in AO control where the control loop might be running at 500Hz. We develop an AO control regime with a second controller stage actuating a second DM controlled in an open loop according to the estimated residual turbulence. Using the open-source COMPASS tool for simulation, we are able to significantly improve the performance using our new regime.

We report on our HOL4 verification of an AI planning algorithm. The algorithm is compositional in the following sense: a planning problem is divided into multiple smaller abstractions, then each of the abstractions is solved, and finally the abstractions' solutions are composed into a solution for the given problem. Formalising the algorithm, which was already quite well understood, revealed nuances in its operation which could lead to computing buggy plans. The formalisation also revealed that the algorithm can be presented more generally, and can be applied to systems with infinite states and actions, instead of only finite ones. Our formalisation extends an earlier model for slightly simpler transition systems, and demonstrates another step towards formal treatments of more and more of the algorithms and reasoning used in AI planning, as well as model checking.

We survey existing rules of thumb, propose novel methods, and comprehensively evaluate a number of solutions to the problem of calculating the cost to serve each location in a single-vehicle transport setting. Cost to serve analysis has applications both strategically and operationally in transportation settings. The problem is formally modeled as the traveling salesperson game (TSG), a cooperative transferable utility game in which agents correspond to locations in a traveling salesperson problem (TSP). The total cost to serve all locations in the TSP is the length of an optimal tour. An allocation divides the total cost among individual locations, thus providing the cost to serve each of them. As one of the most important normative division schemes in cooperative games, the Shapley value gives a principled and fair allocation for a broad variety of games including the TSG. We consider a number of direct and sampling-based procedures for calculating the Shapley value, and prove that approximating the Shapley value of the TSG within a constant factor is NP-hard. Treating the Shapley value as an ideal baseline allocation, we survey six proxies for it that are each relatively easy to compute. Some of these proxies are rules of thumb and some are procedures international delivery companies use(d) as cost allocation methods. We perform an experimental evaluation using synthetic Euclidean games as well as games derived from real-world tours calculated for scenarios involving fast-moving goods; where deliveries are made on a road network every day. We explore several computationally tractable allocation techniques that are good proxies for the Shapley value in problem instances of a size and complexity that is commercially relevant.

From an automated planning perspective the problem of practical mobile robot control in realistic environments poses many important and contrary challenges. On the one hand, the planning process must be lightweight, robust, and timely. Over the lifetime of the robot it must always respond quickly with new plans that accommodate exogenous events, changing objectives, and the underlying unpredictability of the environment. On the other hand, in order to promote efficient behaviours the planning process must perform computationally expensive reasoning about contingencies and possible revisions of subjective beliefs according to quantitatively modelled uncertainty in acting and sensing. Towards addressing these challenges, we develop a continual planning approach that switches between using a fast satisficing "classical" planner, to decide on the overall strategy, and decision-theoretic planning to solve small abstract subproblems where deeper consideration of the sensing model is both practical, and can significantly impact overall performance. We evaluate our approach in large problems from a realistic robot exploration domain.

Robots must perform tasks efficiently and reliably while acting under uncertainty. One way to achieve efficiency is to give the robot commonsense knowledge about the structure of the world. Reliable robot behaviour can be achieved by modelling the uncertainty in the world probabilistically. We present a robot system that combines these two approaches and demonstrate the improvements in efficiency and reliability that result. Our first contribution is a probabilistic relational model integrating common-sense knowledge about the world in general, with observations of a particular environment. Our second contribution is a continual planning system which is able to plan in the large problems posed by that model, by automatically switching between decision-theoretic and classical procedures. We evaluate our system on object search tasks in two different real-world indoor environments. By reasoning about the trade-offs between possible courses of action with different informational effects, and exploiting the cues and general structures of those environments, our robot is able to consistently demonstrate efficient and reliable goal-directed behaviour. 1

In this paper we address the problem of topologically mapping environments which contain inherent perceptual aliasing caused by repeated environment structures. We propose an approach that does not use motion or odometric information but only a sequence of deterministic measurements observed by traversing an environment. Our algorithm implements a stochastic local search to build a small map which is consistent with local adjacency information extracted from a sequence of observations. Moreover, local adjacency information is incorporated to disambiguate places which are physically different but appear identical to the robots senses. Experiments show that the proposed method is capable of mapping environments with a high degree of perceptual aliasing, and that it infers a small map quickly.

A decision process in which rewards depend on history rather than merely on the current state is called a decision process with non-Markovian rewards (NMRDP). In decision-theoretic planning, where many desirable behaviours are more naturally expressed as properties of execution sequences rather than as properties of states, NMRDPs form a more natural model than the commonly adopted fully Markovian decision process (MDP) model. While the more tractable solution methods developed for MDPs do not directly apply in the presence of non-Markovian rewards, a number of solution methods for NMRDPs have been proposed in the literature. These all exploit a compact specification of the non-Markovian reward function in temporal logic, to automatically translate the NMRDP into an equivalent MDP which is solved using efficient MDP solution methods. This paper presents NMRDPP (Non-Markovian Reward Decision Process Planner), a software platform for the development and experimentation of methods for decision-theoretic planning with non-Markovian rewards. The current version of NMRDPP implements, under a single interface, a family of methods based on existing as well as new approaches which we describe in detail. These include dynamic programming, heuristic search, and structured methods. Using NMRDPP, we compare the methods and identify certain problem features that affect their performance. NMRDPP's treatment of non-Markovian rewards is inspired by the treatment of domain-specific search control knowledge in the TLPlan planner, which it incorporates as a special case. In the First International Probabilistic Planning Competition, NMRDPP was able to compete and perform well in both the domain-independent and hand-coded tracks, using search control knowledge in the latter.

Scientists have been organising the forms of natural life into structured hierarchical systems since Linnaeus in the 18th century. Much more recently, computer scientists have developed a class of languages, called description logics (DL), that are aimed at describing concepts so that they may be automatically classified in hierarchical structures. These languages are being adopted in recent proposals for ontology definition that underly the semantic Web, particularly OWL-DL (Bechofer et al., 2003). In this paper we study the applicability of modern description logics to the application of animal systematics. We would like to improve both the process of scientific classification itself, and the methods for communication and integration of taxonomic knowledge. As a case study, we consider a published scientific treatment of Epopostruma, a genus of Australian Formicidae (ants) (Shattuck, 2000). We focus on expressing the morphological characters of Epopostruma, that is the features that derive from the form, structures, homologies and metamorphoses which characterise an individual. We express these characters in the description logic ALCQHIO/sub R//sup +/(D)/sup -/ underlying OWL-DL. Racer (Haarslev and Moller, 2001) is a readily-available reasoner ALCQHIO/sub R//sup +/(D)/sup -/, and is used in this paper to support the development of the DL application to animal systematics. We have used the native syntax of Racer for DL expressions in this paper. We find that most of the language used in a scientific description is readily adapted to the formal description logic language, with the exception of spatio-temporal elements and some higher-order constructs. We show that the reasoning capability is sufficient for consistency checking and retrieval of taxonomic knowledge. We discuss some benefits of the representation to assist the work of biological systematists.