Additive Pattern Database Heuristics

We explore a method for computing admissible heuristic evaluation functions for search problems. It utilizes pattern databases, which are precomputed tables of the exact cost of solving various subproblems of an existing problem. Unlike standard pattern database heuristics, however, we partition our problems into disjoint subproblems, so that the costs of solving the different subproblems can be added together without overestimating the cost of solving the original problem. Previously, we showed how to statically partition the sliding-tile puzzles into disjoint groups of tiles to compute an admissible heuristic, using the same partition for each state and problem instance. Here we extend the method and show that it applies to other domains as well. We also present another method for additive heuristics which we call dynamically partitioned pattern databases. Here we partition the problem into disjoint subproblems for each state of the search dynamically. We discuss the pros and cons of each of these methods and apply both methods to three different problem domains: the sliding-tile puzzles, the 4-peg Towers of Hanoi problem, and finding an optimal vertex cover of a graph. We find that in some problem domains, static partitioning is most effective, while in others dynamic partitioning is a better choice. In each of these problem domains, either statically partitioned or dynamically partitioned pattern database heuristics are the best known heuristics for the problem

Planning And Scheduling For Large-scaledistributed Systems

Many applications require computing resources well beyond those available on any single system. Simulations of atomic and subatomic systems with application to material science, computations related to study of natural sciences, and computer-aided design are examples of applications that can benefit from the resource-rich environment provided by a large collection of autonomous systems interconnected by high-speed networks. To transform such a collection of systems into a user\u27s virtual machine, we have to develop new algorithms for coordination, planning, scheduling, resource discovery, and other functions that can be automated. Then we can develop societal services based upon these algorithms, which hide the complexity of the computing system for users. In this dissertation, we address the problem of planning and scheduling for large-scale distributed systems. We discuss a model of the system, analyze the need for planning, scheduling, and plan switching to cope with a dynamically changing environment, present algorithms for the three functions, report the simulation results to study the performance of the algorithms, and introduce an architecture for an intelligent large-scale distributed system

Analysis of Generalized Artificial Intelligence Potential through Reinforcement and Deep Reinforcement Learning Approaches

Artificial Intelligence is the next competitive domain; the first nation to develop human level artificial intelligence will have an impact similar to the development of the atomic bomb. To maintain the security of the United States and her people, the Department of Defense has funded research into the development of artificial intelligence and its applications. This research uses reinforcement learning and deep reinforcement learning methods as proxies for current and future artificial intelligence agents and to assess potential issues in development. Agent performance were compared across two games and one excursion: Cargo Loading, Tower of Hanoi, and Knapsack Problem, respectively. Deep reinforcement learning agents were observed to handle a wider range of problems, but behave inferior to specialized reinforcement learning algorithms

Classical Planning in Deep Latent Space

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.Comment: Under review at Journal of Artificial Intelligence Research (JAIR

Adaptive Parallel Iterative Deepening Search

Many of the artificial intelligence techniques developed to date rely on heuristic search through large spaces. Unfortunately, the size of these spaces and the corresponding computational effort reduce the applicability of otherwise novel and effective algorithms. A number of parallel and distributed approaches to search have considerably improved the performance of the search process. Our goal is to develop an architecture that automatically selects parallel search strategies for optimal performance on a variety of search problems. In this paper we describe one such architecture realized in the Eureka system, which combines the benefits of many different approaches to parallel heuristic search. Through empirical and theoretical analyses we observe that features of the problem space directly affect the choice of optimal parallel search strategy. We then employ machine learning techniques to select the optimal parallel search strategy for a given problem space. When a new search task is input to the system, Eureka uses features describing the search space and the chosen architecture to automatically select the appropriate search strategy. Eureka has been tested on a MIMD parallel processor, a distributed network of workstations, and a single workstation using multithreading. Results generated from fifteen puzzle problems, robot arm motion problems, artificial search spaces, and planning problems indicate that Eureka outperforms any of the tested strategies used exclusively for all problem instances and is able to greatly reduce the search time for these applications

Gaze transitions when learning with multimedia

Eye tracking methodology is used to examine the influence of interactive multimedia on the allocation of visual attention and its dynamics during learning. We hypothesized that an interactive simulation promotes more organized switching of attention between different elements of multimedia learning material, e.g., textual description and pictorial visualization. Participants studied a description of an algorithm accompanied either by an interactive simulation, self-paced animation, or static illustration. Using a novel framework for entropy-based comparison of gaze transition matrices, results showed that the interactive simulation elicited more careful visual investigation of the learning material as well as reading of the problem description through to its completion

Cross-lingual priming of cognates and interlingual homographs from L2 to L1

Many word forms exist in multiple languages, and can have either the same meaning (cognates) or a different meaning (interlingual homographs). Previous experiments have shown that processing of interlingual homographs in a bilingual’s second language is slowed down by recent experience with these words in the bilingual’s native language, while processing of cognates can be speeded up (Poort et al., 2016; Poort & Rodd, 2019a). The current experiment replicated Poort and Rodd’s (2019a) Experiment 2 but switched the direction of priming: Dutch–English bilinguals (n = 106) made Dutch semantic relatedness judgements to probes related to cognates (n = 50), interlingual homographs (n = 50) and translation equivalents (n = 50) they had seen 15 minutes previously embedded in English sentences. The current experiment is the first to show that a single encounter with an interlingual homograph in one’s second language can also affect subsequent processing in one’s native language. Cross-lingual priming did not affect the cognates. The experiment also extended Poort and Rodd (2019a)’s finding of a large interlingual homograph inhibition effect in a semantic relatedness task in the participants’ L2 to their L1, but again found no evidence for a cognate facilitation effect in a semantic relatedness task. These findings extend the growing literature that emphasises the high level of interaction in a bilingual’s mental lexicon, by demonstrating the influence of L2 experience on the processing of L1 words. Data, scripts, materials and pre-registration available via https://osf.io/2swyg/?view_only=b2ba2e627f6f4eaeac87edab2b59b236

Proceedings of the Workshop on Change of Representation and Problem Reformulation

The proceedings of the third Workshop on Change of representation and Problem Reformulation is presented. In contrast to the first two workshops, this workshop was focused on analytic or knowledge-based approaches, as opposed to statistical or empirical approaches called 'constructive induction'. The organizing committee believes that there is a potential for combining analytic and inductive approaches at a future date. However, it became apparent at the previous two workshops that the communities pursuing these different approaches are currently interested in largely non-overlapping issues. The constructive induction community has been holding its own workshops, principally in conjunction with the machine learning conference. While this workshop is more focused on analytic approaches, the organizing committee has made an effort to include more application domains. We have greatly expanded from the origins in the machine learning community. Participants in this workshop come from the full spectrum of AI application domains including planning, qualitative physics, software engineering, knowledge representation, and machine learning