Robots depend on captured images for perceiving the environment. A robot can replace a human in capturing quality photographs for publishing. In this paper, we employ an iterative photo capture by robots (by repositioning itself) to capture good quality photographs. Our image quality assessment approach is based on few high level features of the image combined with some of the aesthetic guidelines of professional photography. Our system can also be used in web image search applications to rank images. We test our quality assessment approach on a large and diversified dataset and our system is able to achieve a classification accuracy of 79%. We assess the aesthetic error in the captured image and estimate the change required in orientation of the robot to retake an aesthetically better photograph. Our experiments are conducted on NAO robot with no stereo vision. The results demonstrate that our system can be used to capture professional photographs which are in accord with the human professional photography.

We study a pursuit-evasion game in which one or more cops try to capture a robber by moving onto the robber's current location. All players have equal maximum velocities. They can observe each other at all times. We show that three cops can capture the robber in any polygonal environment (which can contain any finite number of holes).

In contrast to the studied in single-period optimization [Miller and Wagner, standard approach that studies the expected performance, 1965; Prékopa, 1970]. However, little has been done in we consider the policy that maximizes the context of sequential decision problem including MDPs. the probability of achieving a predetermined target The standard approaches in risk-averse MDPs include maximization performance, a criterion we term probabilistic of expected utility function [Bertsekas, 1995], goal Markov decision processes. We show that and optimization of a coherent risk measure [Riedel, 2004; this problem is NPhard, but can be solved using a Le Tallec, 2007]. Both approaches lead to formulations that pseudo-polynomial algorithm. We further consider can not be solved in polynomial time, except for special a variant dubbed "chance-constraint Markov decision cases including exponential utility function [Chung and Sobel, problems," that treats the probability of achieving 1987], piecewise linear utility function with a single target performance as a constraint instead of the break down point [Liu and Koenig, 2005], and risk measures maximizing objective. This variant is NPhard, but that can be reduced to robust MDPs satisfying the socalled can be solved in pseudo-polynomial time.

We propose a novel approach for solving unary SAS+ planning problems. This approach extends an SAS+ instance with new state variables representing intentions about how each original state variable will be used or changed next, and splits the original actions into several stages of intention followed by eventual execution. The result is a new SAS+ instance with the same basic solutions as the original. While the transformed problem is larger, it has additional structure that can be exploited to reduce the branching factor, leading to reachable state spaces that are many orders of magnitude smaller (and hence much faster planning) in several test domains with acyclic causal graphs.

This paper investigates the effectiveness of two state representations, CNF and DNF, in contingent planning. To this end, we developed a new contingent planner, called CNFct, using the AND/OR forward search algorithm PrAO [To et al., 2011] and an extension of the CNF representation of [To et al., 2010] for conformant planning to handle nondeterministic and sensing actions for contingent planning. The study uses CNFct and DNFct [To et al., 2011] and proposes a new heuristic function for both planners. The experiments demonstrate that both CNFct and DNFct offer very competitive performance in a large range of benchmarks but neither of the two representations is a clear winner over the other. The paper identifies properties of the representation schemes that can affect their performance on different problems.

Planning under uncertainty for multiagent systems can be formalized as a decentralized partially observable Markov decision process. We advance the state of the art for optimal solution of this model, building on the Multiagent A* heuristic search method. A key insight is that we can avoid the full expansion of a search node that generates a number of children that is doubly exponential in the node's depth. Instead, we incrementally expand the children only when a next child might have the highest heuristic value. We target a subsequent bottleneck by introducing a more memory-efficient representation for our heuristic functions. Proof is given that the resulting algorithm is correct and experiments demonstrate a significant speedup over the state of the art, allowing for optimal solutions over longer horizons for many benchmark problems.

We study the relationship between optimal planning algorithms, in the form of (iterative deepening) A* with (forward) state-space search, and the reduction of the problem to SAT. Our results establish a strict dominance relation between the two approaches: any iterative deepening A* search can be efficiently simulated in the SAT framework, assuming that the heuristic has been encoded in the SAT problem, but the opposite is not possible as A* and IDA* searches sometimes take exponentially longer.

Plan recognition is the problem of inferring the goals and plans of an agent from partial observations of her behavior. Recently, it has been shown that the problem can be formulated and solved using planners, reducing plan recognition to plan generation. In this work, we extend this model-based approach to plan recognition to the POMDP setting, where actions are stochastic and states are partially observable. The task is to infer a probability distribution over the possible goals of an agent whose behavior results from a POMDP model. The POMDP model is shared between agent and observer except for the true goal of the agent that is hidden to the observer. The observations are action sequences O that may contain gaps as some or even most of the actions done by the agent may not be observed. We show that the posterior goal distribution P ( G | O ) can be computed from the value function V G ( b ) over beliefs b  generated by the POMDP planner for each possible goal G. Some extensions of the basic framework are discussed, and a number of experiments are reported.

Classical planning has been notably successful in synthesizing finite plans to achieve states where propositional goals hold. In the last few years, classical planning has also been extended to incorporate temporally extended goals, expressed in temporal logics such as LTL, to impose restrictions on the state sequences generated by finite plans. In this work, we take the next step and consider the computation of infinite plans for achieving arbitrary LTL goals. We show that infinite plans can also be obtained efficiently by calling a classical planner once over a classical planning encoding that represents and extends the composition of the planning domain and the Buchi automaton representing the goal. This compilation scheme has been implemented and a number of experiments are reported.

This paper considers a constraint-based scheduling approach to the flexible jobshop, a generalization of the traditional jobshop scheduling where activities have a choice of machines. It studies both large neighborhood (LNS) and adaptive randomized decomposition (ARD) schemes, using random, temporal, and machine decompositions. Empirical results on standard benchmarks show that, within 5 minutes, both LNS and ARD produce many new best solutions and are about 0.5% in average from the best-known solutions. Moreover, over longer runtimes, they improve 60% of the best-known solutions and match the remaining ones. The empirical results also show the importance of hybrid decompositions in LNS and ARD.