1,721,096 research outputs found
Anchor Search: A Unified Framework for Unbounded Bidirectional Search
Full text linkIn recent years, significant strides in optimal bidirectional heuristic search (Bi-HS) have deepened our theoretical understanding and boosted performance. Yet, algorithms for Bi-HS in unbounded suboptimal scenarios remains largely unexplored. Despite leveraging front-to-end (F2E) and front-to-front (F2F) bidirectional search for optimal algorithms, adapting them for unbounded suboptimal search remains an open challenge. We introduce a novel framework for suboptimal Bi-HS, called anchor search, and use it to derive new algorithms. Additionally, we propose using pattern databases (PDBs) as differential heuristics (DHs) to construct F2F heuristics—a necessity for F2F searches. Our experiments evaluate six anchor search algorithms across diverse domains, with a subset of them outperforming existing methods
Revisiting the Theory and Practice of Bidirectional and Suboptimal Heuristic Search Algorithms
Full text linkHeuristic Search is a general problem-solving method widely used in artificial intelligence (AI). This thesis presents contributions to heuristic search, including contributions to bidirectional optimal search and unidirectional suboptimal search.
For bidirectional optimal search, this thesis presents fundamental theory for the analysis of necessary expansions and the minimum possible number of node expansions needed to solve a given problem in front-to-end heuristic search. A new front-to-end heuristic search algorithm, NBS, which has a worst case guarantee for the number of node expansions, is also presented in this thesis.
For unidirectional suboptimal search, this thesis presents the theory of best-first bounded-suboptimal search using priority functions that do not need to perform state re-expansions as long as the search heuristic is consistent. Also, particular priority functions, such as piecewise linear functions are presented in this document. Several new priority functions can significantly outperform existing approaches according to empirical results
Navigation in Adversarial Environments Guided by PRA* and a Local RL Planner
Full text linkReal-time strategy games require players to respond to short-term challenges (micromanagement) and long-term objectives (macromanagement) simultaneously to win. However, many players excel at one of these skills but not both. This research studies whether the burden of micromanagement can be reduced on human players through delegation of responsibility to autonomous agents. In particular, this research proposes an adversarial navigation architecture that enables units to autonomously navigate through places densely populated with enemies by learning to micromanage itself. Our approach models the adversarial pathfinding problem as a Markov Decision Process (MDP) and trains an agent with reinforcement learning on this MDP. We observed that our approach resulted in the agent taking less damage from adversaries while traveling shorter paths, compared to previous approaches on adversarial navigation. Our approach is also efficient in memory use and computation time. Interestingly, the agent using the proposed approach outperformed baseline approaches while navigating through environments that are significantly different from the training environments. Furthermore, when the game design is modified, the agent discovers effective alternate strategies considering the updated design without any changes in the learning framework. This property is particularly useful because in game development the design of a game is often updated iteratively
Building Helpful Virtual Agents Using Plan Recognition and Planning
No full textThis paper presents a new model of cooperative behaviour based on the interaction of plan recognition and automated planning. Based on observations of the actions of an "initiator" agent, a "supporter" agent uses plan recognition to hypothesize the plans and goals of the initiator. The supporter agent then proposes and plans for a set of subgoals it will achieve to help the initiator. The approach is demonstrated in an open-source, virtual robot platform
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Suboptimal Search with Dynamic Distribution of Suboptimality
No full textIn bounded-suboptimal heuristic search, the aim is to find a solution path within a given bound as quickly as possible, which is crucial when computational resources are limited. Recent research has demonstrated Weighted A* variants such as XDP that find bounded suboptimal solutions without needing to perform state re-expansions; in addition to a new version of WA*, other variants work by shifting where the suboptimality in the search is allowed. However, the suboptimality distribution is fixed before the search begins. This thesis introduces a new framework that allows suboptimality to be dynamically distributed at runtime, based on the properties of the search. Experiments show this dynamic policy consistently outperforms existing algorithms across a diverse set of domains, particularly those with dynamic costs
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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