Parallelization of non-admissible search algorithms such as GBFS poses a challenge because straightforward parallelization can result in search behavior which significantly deviates from sequential search. Previous work proposed PUHF, a parallel search algorithm which is constrained to only expand states that can be expanded by some tie-breaking strategy for GBFS. We show that despite this constraint, the number of states expanded by PUHF is not bounded by a constant multiple of the number of states expanded by sequential GBFS with the worst-case tie-breaking strategy. We propose and experimentally evaluate One Bench At a Time (OBAT), a parallel greedy search which guarantees that the number of states expanded is within a constant factor of the number of states expanded by sequential GBFS with some tie-breaking policy.

In order to understand and control the search behavior of parallel search, recent work has proposed a class of constrained parallel greedy best-first search algorithms which only expands states that satisfy some constraint. However, enforcing such constraints can be costly, as threads must be waiting idly until a state that satisfies the expansion constraint is available. We propose an improvement to constrained parallel search which decouples state generation and state evaluation and significantly improves state evaluation rate, resulting in better search performance.

While parallelization of A* is fairly well-understood, parallelization of GBFS has been much less understood. Recent work has proposed PUHF, a parallel GBFS which restricts search to exploration of the Bench Transition System (BTS), which is the set of states that can be expanded by GBFS under some tie-breaking policy. However, PUHF causes threads to spend much of the time waiting so that only states which are guaranteed to be in the BTS are expanded. We propose improvements to PUHF which significantly reduce idle time and allow more rapid exploration of the BTS, resulting in better search performance.

The notion of a population with individuals which are replaced by newly generated individuals is a pervasive idea in differential evolution (DE). Techniques such as archives have been previously proposed to supplement the central idea of a population, motivated by improved performance compared to a simple population structure. We consider Unbounded DE (UDE), which uses a population where individuals are never replaced, and the population monotonically grows as new individuals are generated. Behaviors similar to standard populations, as well as previous population modifications such as archives can be implemented within the UDE framework by implementing specific selection policies. We show experimentally that UDE can be configured to be competitive with standard DE as well as adaptive DE algorithms, showing that the traditional notion of replacement is not necessary for good performance.

Some modern computing architectures such as those which use GPUs offer significantly more computational capability in single-precision than double-precision floating point. However, when using single-precision math, evolutionary optimization algorithms such as differential evolution are much more prone to collision, where new individuals created by reproduction operators are duplicates of previously generated individuals. We show experimentally that on the CEC2014 single objective benchmarks and the BBOB benchmarks, search using the SHADE adaptive DE can waste more than 20% of the fitness evaluations due to collisions. We (1) discuss the causes of this collision, (2) detect collisions using hash to avoid unnecessary evaluations, and (3) propose a restart method based on collisions.

Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems such as planners. We propose Latplan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), Latplan learns a complete propositional PDDL action model of the environment. Later, when a pair of images representing the initial and the goal states (planning inputs) is given, Latplan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. We evaluate Latplan using image-based versions of 6 planning domains: 8-puzzle, 15-Puzzle, Blocksworld, Sokoban and Two variations of LightsOut.

The introduction of success-history based adaptation in SHADE, a variant of JADE, resulted in a significant advance in the performance of adaptive differential evolution. Many variants of SHADE which use the same success history mechanism have been proposed, but the success history mechanism has remained poorly understood. We revisit use of the success history based adaptation, and show experimentally that the standard approach to sampling from the success history in SHADE may not be as vital to performance as previously assumed. We show that EnJADE, a simple, new variant of JADE which maintains an ensemble of control parameter distribution means, can outperform SHADE on the CEC14 benchmark suite. We also show the effectiveness of the new ensemble-based approach when combined with linear population reduction.

Although symbolic bidirectional search is successful in optimal classical planning, state-of-the-art satisficing planners do not use bidirectional search. Previous bidirectional search planners for satisficing planning behaved similarly to a trivial portfolio, which independently executes forward and backward search without the desired ``meet-in-the-middle'' behavior of bidirectional search where the forward and backward search frontiers intersect at some point relatively far from the forward and backward start states. In this paper, we propose Top-to-Top Bidirectional Search (TTBS), a new bidirectional search strategy with front-to-front heuristic evaluation. We show that TTBS strongly exhibits ``meet-in-the-middle'' behavior and can solve instances solved by neither forward nor backward search on a number of domains.

Many differential evolution (DE) algorithms with various parameter control methods (PCMs) have been proposed. However, previous studies usually considered PCMs to be an integral component of a complex DE algorithm. Thus, the characteristics and performance of each method are poorly understood. We present an in-depth review of 24 PCMs for the scale factor and crossover rate in DE and a large-scale benchmarking study. We carefully extract the 24 PCMs from their original, complex algorithms and describe them according to a systematic manner. Our review facilitates the understanding of similarities and differences between existing, representative PCMs. The performance of DEs with the 24 PCMs and 16 variation operators is investigated on 24 black-box benchmark functions. Our benchmarking results reveal which methods exhibit high performance when embedded in a standardized framework under 16 different conditions, independent from their original, complex algorithms. We also investigate how much room there is for further improvement of PCMs by comparing the 24 methods with an oracle-based model, which can be considered to be a conservative lower bound on the performance of an optimal method.

It is known that seemingly small details such as tie-breaking among nodes with the same f-cost can significantly affect the performance of a best-first search algorithm on many domains (Asai and Fukunaga 2017). In this paper, we show that low-level algorithmic details of domain-independent planning heuristics can have a surprisingly large impact on search performance. As a case study, we consider the well-known FF heuristic (hff ) (Hoffmann and Nebel 2001).

Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems such as planners. We propose LatPlan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), and a pair of images representing the initial and the goal states (planning inputs), LatPlan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. The contribution of this paper is twofold: (1) State Autoencoder, which finds a propositional state representation of the environment using a Variational Autoencoder. It generates a discrete latent vector from the images, based on which a PDDL model can be constructed and then solved by an off-the-shelf planner. (2) Action Autoencoder / Discriminator, a neural architecture which jointly finds the action symbols and the implicit action models (preconditions/effects), and provides a successor function for the implicit graph search. We evaluate LatPlan using image-based versions of 3 planning domains: 8-puzzle, Towers of Hanoi and LightsOut.

Black-box domains where the successor states generated by applying an action are generated by a completely opaque simulator pose a challenge for domain-independent planning. The main computational bottleneck in search-based planning for such domains is the number of calls to the black-box simulation. We propose a method for significantly reducing the number of calls to the simulator by the search algorithm by detecting and pruning sequences of actions which are dominated by others. We apply our pruning method to Iterated Width and breadth-first search in domain-independent black-box planning for Atari 2600 games in the Arcade Learning Environment (ALE), adding our pruning method significantly improves upon the baseline algorithms.

While a large number of adaptive Differential Evolution (DE) algorithms have been proposed, their Parameter Adaptation Methods (PAMs) are not well understood. We propose a Target function-based PAM simulation (TPAM) framework for evaluating the tracking performance of PAMs. The proposed TPAM simulation framework measures the ability of PAMs to track predefined target parameters, thus enabling quantitative analysis of the adaptive behavior of PAMs. We evaluate the tracking performance of PAMs of widely used five adaptive DEs (jDE, EPSDE, JADE, MDE, and SHADE) on the proposed TPAM, and show that TPAM can provide important insights on PAMs, e.g., why the PAM of SHADE performs better than that of JADE, and under what conditions the PAM of EPSDE fails at parameter adaptation.

This document gathers the abstracts for the papers that were presented as part of the Previously Published Paper Track.

We investigate GPU-based parallelization of Iterative-Deepening A* (IDA*). We show that straightforward thread-based parallelization techniques which were previously proposed for massively parallel SIMD processors perform poorly due to warp divergence and load imbalance. We propose Block-Parallel IDA* (BPIDA*), which assigns the search of a subtree to a block (a group of threads with access to fast shared memory) rather than a thread. On the 15-puzzle, BPIDA* on a NVIDIA GRID K520 with 1536 CUDA cores achieves a speedup of 4.98 compared to a highly optimized sequential IDA* implementation on a Xeon E5-2670 core.

Best-first search algorithms such as A* need to apply tie-breaking strategies in order to decide which node to expand when multiple search nodes have the same evaluation score. We investigate and improve tie-breaking strategies for cost-optimal search using A*. We first experimentally analyze the performance of common tie-breaking strategies that break ties according to the heuristic value of the nodes. We find that the tie-breaking strategy has a significant impact on search algorithm performance when there are 0-cost operators that induce large plateau regions in the search space. Based on this, we develop two new classes of tie-breaking strategies. We first propose a depth diversification strategy which breaks ties according to the distance from the entrance to the plateau, and then show that this new strategy significantly outperforms standard strategies on domains with 0-cost actions. Next, we propose a new framework for interpreting A* search as a series of satisficing searches within plateaus consisting of nodes with the same f-cost. Based on this framework, we investigate a second, new class of tie-breaking strategy, a multi-heuristic tie-breaking strategy which embeds inadmissible, distance-to-go variations of various heuristics within an admissible search. This is shown to further improve the performance in combination with the depth metric.

Parallel best-first search algorithms such as Hash Distributed A* (HDA*) distribute work among the processes using a global hash function. We analyze the search and communication overheads of state-of-the-art hash-based parallel best-first search algorithms, and show that although Zobrist hashing, the standard hash function used by HDA*, achieves good load balance for many domains, it incurs significant communication overhead since almost all generated nodes are transferred to a different processor than their parents. We propose Abstract Zobrist hashing, a new work distribution method for parallel search which, instead of computing a hash value based on the raw features of a state, uses a feature projection function to generate a set of abstract features which results in a higher locality, resulting in reduced communications overhead. We show that Abstract Zobrist hashing outperforms previous methods on search domains using hand-coded, domain specific feature projection functions. We then propose GRAZHDA*, a graph-partitioning based approach to automatically generating feature projection functions. GRAZHDA* seeks to approximate the partitioning of the actual search space graph by partitioning the domain transition graph, an abstraction of the state space graph. We show that GRAZHDA* outperforms previous methods on domain-independent planning.

Despite recent improvements in search techniques for cost-optimal classical planning, the exponential growth of the size of the search frontier in A* is unavoidable. We investigate tiebreaking strategies for A*, experimentally analyzing the performance of standard tiebreaking strategies that break ties according to the heuristic value of the nodes. We find that tiebreaking has a significant impact on search algorithm performance when there are zero-cost operators that induce large plateau regions in the search space. We develop a new framework for tiebreaking based on a depth metric which measures distance from the entrance to the plateau, and propose a new, randomized strategy which significantly outperforms standard strategies on domains with zero-cost actions.

In the petroleum industry, accurate oil reservoir models are crucial in the decision making process. One critical step in reservoir modeling is History Matching (HM), where the parameters of a reservoir model are adjusted in order to improve its accuracy and enhance future prediction. Recent works applied evolutionary algorithms (EAs) such as GA, DE and PSO for the HM problem, but they have been limited to classical versions of these algorithms. A significant obstacle to applying EAs to HM is that each call to the fitness function requires an expensive simulation, making it difficult to tune the control parameters for EAs in order to obtain the best performance. We apply and evaluate state-of-the-art, adaptive differential algorithms (SHADE and jDE), as well as non-adaptive evolutionary algorithms (standard DE, PSO) that have been tuned using standard black-box benchmark functions as training instances. Both of these approaches result in significant improvements compared to standard methods in the HM literature. We also apply fitness distance correlation analysis to the search space explored by our algorithms in order to better understand the landscape of the HM problem.

This paper presents a parameter tuning study of Differential Evolution (DE) algorithms, including both standard DE as well as variants of the state-of-the-art adaptive DE, SHADE for both cheap and expensive optimization scenarios. Using the algorithm configuration tool SMAC, the DE variants are tuned independently for three different scenarios: expensive (102× D evaluations), medium (104× D evaluations), cheap (105× D evaluations), where D is the benchmark problem dimensionality. Each of these tuned parameter settings is then tested under both cheap and expensive scenarios, which enables us to analyze the effect of both the tuning and test scenario on the performance of the tuned algorithm. We evaluate restarting variants of DE (R-DE), as well as restarting variants of SHADE (R-SHADE) and L-SHADE (RL-SHADE). For the parameter tuning phase, we use the CEC2014 benchmarks as training problems, and for the testing phase, we use all 24 problems from the BBOB benchmark set. We also compare these DE variants with state-of-the-art restart CMA-ES variants (HCMA, BIPOPCMA-ES, and IPOP-CMA-ES). For both cheap and expensive scenarios, DE algorithms perform very well for low-dimensional problems. In particular, for the expensive scenario, the simple, restarting DE (R-DE) performs quite well, and on the cheap scenario, RL-SHADE performs well.

We propose a new integer-linear programming model for the delete relaxation in cost-optimal planning. While a straightforward IP for the delete relaxation is impractical, our enhanced model incorporates variable reduction techniques based on landmarks, relevance-based constraints, dominated action elimination, immediate action application, and inverse action constraints, resulting in an IP that can be used to directly solve delete-free planning problems. We show that our IP model is competitive with previous state-of-the-art solvers for delete-free problems. The LP-relaxation of the IP model is often a very good approximation to the IP, providing an approach to approximating the optimal value of the delete-free task that is complementary to the well-known LM-cut heuristic. We also show that constraints that partially consider delete effects can be added to our IP/LP models. We embed the new IP/LP models into a forward-search based planner, and show that the performance of the resulting planner on standard IPC benchmarks is comparable with the state-of-the-art for cost-optimal planning.

SHADE is an adaptive DE which incorporates success-history based parameter adaptation and one of the state-of-the-art DE algorithms. This paper proposes L-SHADE, which further extends SHADE with Linear Population Size Reduction (LPSR), which continually decreases the population size according to a linear function. We evaluated the performance of L-SHADE on CEC2014 benchmarks and compared its search performance with state-of-the-art DE algorithms, as well as the state-of-the-art restart CMA-ES variants. The experimental results show that L-SHADE is quite competitive with state-of-the-art evolutionary algorithms.

We propose a new integer-linear programming model for the delete relaxation in cost-optimal planning. While a naive formulation of the delete relaxation as IP is impractical, our model incorporates landmarks and relevance-based constraints, resulting in an IP that can be used to directly solve the delete relaxation. We show that our IP model outperforms the previous state-of-the-art solver for delete-free problems.We then use LP relaxation of the IP as a heuristics for a forward search planner, and show that our LP-based solver is competitive with the state-of-the-art for cost-optimal planning.

Most state-of-the-art Differential Evolution (DE) algorithms are adaptive DEs with online parameter adaptation. We investigate the behavior of adaptive DE on a class of hybrid functions, where independent groups of variables are associated with different component objective functions. An experimental evaluation of 3 state-of-the-art adaptive DEs (JADE, SHADE, jDE) shows that hybrid functions are "adaptive-DE-hard". That is, adaptive DEs have significant failure rates on these new functions. In-depth analysis of the adaptive behavior of the DEs reveals that their parameter adaptation mechanisms behave in a pathological manner on this class of problems, resulting in over-adaptation for one of the components of the hybrids and poor overall performance. Thus, this class of deceptive benchmarks pose a significant challenge for DE.

Differential Evolution is a simple, but effective approach for numerical optimization. Since the search efficiency of DE depends significantly on its control parameter settings, there has been much recent work on developing self-adaptive mechanisms for DE. We propose a new, parameter adaptation technique for DE which uses a historical memory of successful control parameter settings to guide the selection of future control parameter values. The proposed method is evaluated by comparison on 28 problems from the CEC2013 benchmark set, as well as CEC2005 benchmarks and the set of 13 classical benchmark problems. The experimental results show that a DE using our success-history based parameter adaptation method is competitive with the state-of-the-art DE algorithms.

This paper presents a large-scale, empirical evaluation of a Random, Heterogeneous Island-Model (RHIM) for evolutionary algorithms (EAs), where the control parameter values are independently, randomly assigned for each island that has recently been proposed by Gong and Fukunaga as a method for configuring island-model evolutionary algorithms in situations where it is not possible to expend the resources to carefully tune control parameters for a particular application. We apply RHIM to standard DE, JADE (an adaptive DE), and real-coded genetic algorithms. Evaluations are performed on standard black-box function optimization benchmarks, as well as combinatorial optimization problems (the TSP and QAP). The search efficiency of RHIM is compared to manual tuning of parameter settings for each benchmark problem. Our results with up to 256 islands, show that the search efficiency of RHIM, a method which does not involve any parameter tuning, tends to becomes increasingly competitive with manual parameter tuning as the number of islands increases. The consistent, relatively good performance of RHIM when applied to a variety of EAs on numerous, different benchmark problems suggest that it can be an effective, default method for configuring island-model EAs.

This paper evaluates the performance of Success-History based Adaptive DE (SHADE) on the benchmark set for the CEC2013 Competition on Real-Parameter Single Objective Optimization. SHADE is an adaptive differential algorithm which uses a history-based parameter adaptation scheme. Experimental results on 28 problems from the CEC2013 benchmarks for 10, 30, and 50 dimensions are presented, including measurements of algorithmic complexity. In addition, we investigate the parameter adaptation behavior of SHADE on these instances.

The increasing availability of “utility computing” resources such as clouds, grids, and massively parallel shared clusters can provide practically unlimited processing and memory capacity on demand, at some cost per unit of resource usage. This requires a new perspective in the design and evaluation of parallel search algorithms. Previous work in parallel search implicitly assumed ownership of a cluster with a static amount of CPU cores and RAM, and emphasized wallclock runtime. With utility computing resources, trade-offs between performance and monetary costs must be considered. This paper considers dynamically increasing the usage of utility computing resources until a problem is solved. Efficient resource allocation policies are analyzed in comparison with an optimal allocation strategy. We evaluate our iterative allocation strategy by applying it to the HDA* parallel search algorithm. The experimental results validate our theoretical predictions. They show that, in practice, the costs incurred by iterative allocation are reasonably close to an optimal (but a priori unknown) policy, and are significantly better than the worst-case analytical bounds.

The increasing availability of “utility computing” resources such as clouds, grids, and massively parallel shared clusters can provide practically unlimited processing and memory capacity on demand, at some cost per unit of resource usage. This requires a new perspective in the design and evaluation of parallel search algorithms. Previous work in parallel search implicitly assumed ownership of a cluster with a static amount of CPU cores and RAM, and emphasized wallclock runtime. With utility computing resources, trade-offs between performance and monetary costs must be considered. This paper considers dynamically increasing the usage of utility computing resources until a problem is solved. Efficient resource allocation policies are analyzed in comparison with an optimal allocation strategy. We evaluate our iterative allocation strategy by applying it to the HDA* parallel search algorithm. The experimental results validate our theoretical predictions. They show that, in practice, the costs incurred by iterative allocation are reasonably close to an optimal (but a priori unknown) policy, and are significantly better than the worst-case analytical bounds.

Achieving good performance with a parallel genetic algorithm requires properly configuring control parameters such as mutation rate, crossover rate, and population size. We consider the problem of setting control parameter values in a standard, island-model distributed genetic algorithm. As an alternative to tuning parameters by hand or using a self-adaptive approach, we propose a very simple strategy which statically assigns random control parameter values to each processor. Experiments on benchmark problems show that this simple approach can yield results which are competitive with homogeneous distributed genetic algorithm using parameters tuned specifically for each of the benchmarks.

Guiding visitors through an exhibit space such as a museum is an important, early application for mobile robots, and commercial robots designed for this purpose have become available. We consider the problem of using a single mobile robot to simultaneously direct multiple groups of visitors through a museum or exhibition, and formulate an objective function for this task. We show that an evolutionary robotics approach using a simple, low-fidelity simulator and genetic programming can automatically generate robot controllers which can perform this task better than hand-coded controllers as well as humans in both simulation and on a real robot.

Transposition tables are a well-known method for pruning duplicates in heuristic search. This paper presents a detailed analysis of transposition tables for IDA*. We show that some straightforward implementations of IDA* with transposition tables (IDA*+TT) can result in suboptimal solutions being returned. Furthermore, straightforward implementations of IDA*+TT are not complete. We identify several variants of IDA*+TT which are guaranteed to return the optimal solution, as well as a complete variant. An empirical study shows that IDA*+TT can significantly improve upon the performance of A* in domain-independent planning.

HDA* is a simple, parallelization of A* where work is asynchronously distributed among the nodes by a global hash function. Using up to 1024 cores on a large distributed memory cluster, we evaluate HDA* for a domain-independent planner as well an application-specific 24-puzzle solver. We show that HDA* scales fairly well on a large cluster using up to 1024 cores. Our analysis of the scaling behavior shows that on a cluster of multicore nodes, using only a subset of the available cores and leaving some cores idle can, surprisingly, lead to better results.

Recent work has shown that it is possible to evolve heuristics for solving propositional satisfiability (SAT) problems which are competitive with the best hand-coded heuristics. However, previous work was limited by the computational resources required in order to evolve successful heuristics. In this paper, we describe a massively parallel genetic programming system for evolving SAT heuristics. Runs using up to 5.5 CPU core years of computation were executed, and resulted in new SAT heuristics which significantly outperform hand-coded heuristics.

We propose a new evolutionary algorithm for the multiple knapsack problem (MKP) which uses multiple representations. Previous, successful approaches for the MKP have included a weight-coded, order-based representation, as well as a grouping representation enhanced by a dominance condition to restrict search. We propose a representation-switching genetic algorithm which periodically transforms the representation of individuals between these two representations. We show that this new hybrid algorithm outperforms the previous approaches.

We investigate the effects of communication failures in grid-based, distributed genetic algorithms with various topologies. We evaluated the performance behavior of distributed GAs under varying levels of persistent communication failures, using the sorting network problem as a benchmark application. In this experiment, we find that distributed GA with larger population size is less affected by the lower communication failure rate. However, the effect of lower communication failure on the performance of distributed GA varies with the topologies when population size is small. For all the tree topologies we investigated, when communications failures occur extremely frequently, then a significant performance degradation is observed. However, even in these extreme cases, we show that simple retry/reroute protocols for recovering from communication failure are sufficient to recover most of the performance.

The multiple knapsack problem (MKP) is the problem of assigning (packing) objects of various weights and values (profits) to a set of containers (bins) of various capacities, in order to maximize the total profit of the items assigned to the containers. We propose a new genetic algorithm for the MKP which searches a space of undominated candidate solutions. We compare the new algorithm to previous heuristics for the MKP, as well as alternative evolutionary algorithms, and show experimentally that our new algorithm yields the best performance on difficult instances where item weights and profits are highly correlated.

The development of successful metaheuristic algorithms such as local search for a difficult problem such as satisfiability testing (SAT) is a challenging task. We investigate an evolutionary approach to automating the discovery of new local search heuristics for SAT. We show that several well-known SAT local search algorithms such as Walksat and Novelty are composite heuristics that are derived from novel combinations of a set of building blocks. Based on this observation, we developed CLASS, a genetic programming system that uses a simple composition operator to automatically discover SAT local search heuristics. New heuristics discovered by CLASS are shown to be competitive with the best Walksat variants, including Novelty+. Evolutionary algorithms have previously been applied to directly evolve a solution for a particular SAT instance. We show that the heuristics discovered by CLASS are also competitive with these previous, direct evolutionary approaches for SAT. We also analyze the local search behavior of the learned heuristics using the depth, mobility, and coverage metrics proposed by Schuurmans and Southey.

Many combinatorial optimization problems such as the bin packing and multiple knapsack problems involve assigning a set of discrete objects to multiple containers. These problems can be used to model task and resource allocation problems in multi-agent systems and distributed systms, and can also be found as subproblems of scheduling problems. We propose bin completion, a branch-and-bound strategy for one-dimensional, multicontainer packing problems. Bin completion combines a bin-oriented search space with a powerful dominance criterion that enables us to prune much of the space. The performance of the basic bin completion framework can be enhanced by using a number of extensions, including nogood-based pruning techniques that allow further exploitation of the dominance criterion. Bin completion is applied to four problems: multiple knapsack, bin covering, min-cost covering, and bin packing. We show that our bin completion algorithms yield new, state-of-the-art results for the multiple knapsack, bin covering, and min-cost covering problems, outperforming previous algorithms by several orders of magnitude with respect to runtime on some classes of hard, random problem instances. For the bin packing problem, we demonstrate significant improvements compared to most previous results, but show that bin completion is not competitive with current state-of-the-art cutting-stock based approaches.

Acquisition of science in space applications is shifting from teleoperated gathering to an automated on-board analysis with improvements in the use of memory, CPU, bandwidth and data quality. In this paper, we describe algorithms to autonomously detect dust devils and clouds from a rover and summarize the results. The algorithms meet high hit-to-miss ratios and satisfy strict resource constraints. Both detectors have been uploaded to the Mars exploration rovers (MER). These are the first autonomous science processes in the rovers.

Comparative studies of sets of control parameter values are commonly performed when tuning an evolutionary algorithm for a class of problem instances. The standard approach is to identify the most useful set of control parameter settings for a domain. In this paper, we propose an alternative anytime algorithm portfolio technique in which computational resources are allocated among multiple sets of control parameter value settings. We show a method of optimizing such portfolios by applying a bootstrap sampling approach to a database of individual algorithm performance on instances from a problem distribution. Experiments with genetic algorithms applied to the traveling salesperson domain show that the portfolio approach can yield better performance on a distribution of problem instances than the standard approach of trying to identify the single best configuration for the problem class.

There has been increased research interest in systems composed of multiple autonomous mobile robots exhibiting collective behavior. Groups of mobile robots are constructed, with an aim to studying such issues as group architecture, resource conflict, origin of cooperation, learning, and geometric problems. As yet, few applications of collective robotics have been reported, and supporting theory is still in its formative stages. In this paper, the authors give a critical survey of existing works and discuss open problems in this field, emphasizing the various theoretical issues that arise in the study of cooperative robotics. The authors describe the intellectual heritages that have guided early research, as well as possible additions to the set of existing motivations.

We extend an earlier automatic motion-synthesis algorithm for physically realistic articulated figures in several ways. First, we summarize several incremental improvements to the original algorithm that improve its efficiency significantly and provide the user with some ability to influence what motions are generated. These techniques can be used by an animator to achieve a desired movement style, or they can be used to guarantee variety in the motions synthesized over several runs of the algorithm. Second, we report on new mechanisms that support the concatenation of existing, automatically generated motion controllers to produce complex, composite movement. Finally, we describe initial work on generalizing the techniques from 2D to 3D articulated figures. Taken together, these results illustrate the promise and challenges afforded by the controller-based approach to automatic motion synthesis for computer animation.