Reviewerly/exHarmony huggingface dataset:
OpenNERO: A Game Platform for AI Research and Education

OpenNERO is an open source game platform designed for game AI research. The software package combines features commonly available in modern game engines (such as 3D graphics, physics simulation, 3D audio rendering, networked play, and a powerful scripting interface) with an easy to use API and tools for defining machine learning tasks, environments, and agents. Flexibility and ease of use of the system are demonstrated by following the process of creating a machine learning game from scratch. The scalability of the platform is tested through the implementation of the existing NERO machine learning game using the new tools.
Using Natural Language to Manage NPC Dialog

In this document, we describe our work applying natural language (NL) technologies to improve non-player character (NPC) dialog interactions in games, specifically role-playing games (RPGs). Our approach is to adapt the standard dialog menu interaction so that the menu items are dynamically-generated during game runtime rather than scripted during development time. In our system, menu items are constructed by manipulating abstract semantic representations stored in the NPC knowledgebase, converting them into NL text, and then ranking them so that the most relevant items are placed at the top of the menu. We demonstrate our approach in the context of a small RPG.
Representing Pattern Databases with Succinct Data Structures

In this paper we describe novel representations for precomputed heuristics based on Level-Ordered Edge Sequence (LOES) encodings. We introduce compressed LOES, an extension to LOES that enables more aggressive compression of the state-set representation. We evaluate the novel repre- sentations against the respective perfect-hash and binary decision diagram (BDD) representations of pattern databases in a variety of STRIPS domains.
Finding Bounded Suboptimal Multi-Agent Path Planning Solutions Using Increasing Cost Tree Search (Extended Abstract)

The Increasing Cost Tree Search (ICTS) algorithm is used to produce optimal solutions to the multi-agent path finding problem (MAPF). In this problem, multiple agents are trying to reach their goals without conflicting with each other, while minimizing the total cost of the paths. ICTS has been shown to be very effective in finding optimal solutions. In this paper we consider the problem of finding solutions with bounded suboptimality by changing the order in which ICTS searches its increasing cost tree. With a variety of strategies, we are unable to consistently and significantly reduce the cost of ICTS. Further experimentation suggests why significantly more work is needed to modify ICTS to find suboptimal solutions.
Extracting Meta Statements from the Blogosphere

Information extraction systems have been recently proposed for organizing and exploring content in large online text corpora as information networks. In such networks, the nodes are named entities (e.g., people, organizations) while the edges correspond to statements indicating relations among such entities. To date, such systems extract rather primitive networks, capturing only those relations which are expressed by direct statements. In many applications, it is useful to also extract more subtle relations which are often expressed as meta statements in the text. These can, for instance provide the context for a statement (e.g., “Google acquired YouTube on October 2006”), or repercussion about a statement (e.g., “The US condemned Russia’s invasion of Georgia”). In this work, we report on a system for extracting relations expressed in both direct statements as well as in meta statements. We propose a method based on Conditional Random Fields that explores syntactic features to extract both kinds of statements seamlessly. We follow the Open Information Extraction paradigm, where a classifier is trained to recognize any type of relation instead of specific ones. Finally, our results show substantial improvements over a state-of-the-art information extraction system, both in terms of accuracy and, especially, recall.
Hide and Sneak: Story Generation with Characters that Perceive and Assume

We describe the design of a perception system for the characters in the Virtual Storyteller (VST), a character-centric story generation system. Previously, these characters were omniscient; stories involving sneaking and deception could not be generated. To remedy this, we limited the characters' visual perception using simple rules. We enabled the characters to make assumptions about the story world, so they can plan toward goals in spite of incomplete knowledge. Using the distinction between the character and actor roles of agents in the VST, we can use the assumptions to steer the story plot.
Handling Goal Utility Dependencies in a Satisfiability Framework

Goal utility dependencies arise when the utility of achieving a goal depends on the other goals that are achieved with it. This complicates the planning procedure because achieving a new goal can potentially alter the utilities of all the other goals currently achieved. In this paper, we present an encoding procedure that enables general-purpose Max-SAT solvers to be used to solve planning problems with goal utility dependencies. We compare this approach to one using integer programming via an empirical evaluation using benchmark problems from past international planning competitions. Our results indicate that this approach is competitive and sometimes more successful than an integer programming one -- solving two to three times more subproblems in some domains, while being outperformed by only a significantly smaller margin in others.
Balancing Exploration and Exploitation in Classical Planning

Successful heuristic search planners for satisficing planning like FF or LAMA are usually based on one or more best first search techniques. Recent research has led to planners like Arvand, Roamer or Probe, where novel techniques like Monte-Carlo Random Walks extend the traditional exploitation-focused best first search by an exploration component. The UCT algorithm balances these contradictory incentives and has shown tremendous success in related areas of sequential decision making but has never been applied to classical planning yet. We make up for this shortcoming by applying the Trial-based Heuristic Tree Search framework to classical planning. We show how to model the best first search techniques Weighted A* and Greedy Best First Search with only three ingredients: action selection, initialization and backup function. Then we use THTS to derive four versions of the UCT algorithm that differ in the used backup functions. The experimental evaluation shows that our main algorithm, GreedyUCT*, outperforms all other algorithms presented in this paper, both in terms of coverage and quality.
Unifying An Introduction To Artificial Intelligence Course Through Machine Learning Laboratory Experiences

This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses.The project involves the development of an adaptable framework for the presentation of core AI topics.This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course.Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems.The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering.In this paper we will present our approach, an overview of the project, and the hands-on laboratory modules.Our preliminary experiences incorporating these modules into our introductory AI course will also be presented.
Agent Architecture in Social Games — The Implementation of Subsumption Architecture in Diplomacy

Social games are challenging for AI research because they involve not only the mechanism of game (taking actions, planning, etc), but also the social aspects including communicating, cooperating and reaching agreements. Some social games also require cunning, duplicity, or bad faith. In this paper we present the implementation of a prototype that utilizes the properties of the subsumption architecture to provide an interesting computer opponent in a social board game — Diplomacy. The evaluation result indicates that the subsumption architecture is appropriate for social games.
Using Group Membership Markers for Group Identification

We describe a system for automatically ranking documents by degree of militancy, designed as a tool both for finding militant websites and prioritizing the data found. We compare three ranking systems, one employing a small hand-selected vocabulary based on group membership markers used by insiders to identify members and member properties (us) and outsiders and threats (them), one with a much larger vocabulary, and another with a small vocabulary chosen by Mutual Information. We use the same vocabularies to build classifiers. The ranker that achieves the best correlations with human judgments uses the small us-them vocabulary. We confirm and extend recent results in sentiment analysis (paltoglou 2010), showing that a feature-weighting scheme taken from classical IR (TFIDF) produces the best ranking system; we also find, surprisingly, that adjusting these weights with SVM training, while producing a better classifier, produces a worse ranker. Increasing vocabulary size similarly improves classification (while worsening ranking).
Collective Nominal Semantic Role Labeling for Tweets

Tweets have become an increasingly popular source of fresh information. We investigate the task of Nominal Semantic Role Labeling (NSRL) for tweets, which aims to identify predicate-argument structures defined by nominals in tweets. Studies of this task can help fine-grained information extraction and retrieval from tweets. There are two main challenges in this task: 1) The lack of information in a single tweet, rooted in the short and noisy nature of tweets; and 2) recovery of implicit arguments. We propose jointly conducting NSRL on multiple similar tweets using a graphical model, leveraging the redundancy in tweets to tackle these challenges. Extensive evaluations on a human annotated data set demonstrate that our method outperforms two baselines with an absolute gain of 2.7% in F1.
Mission-Based Scenario Modeling and Generation for Virtual Training

Automated scenario generation for virtual training has become an emerging research problem, as manual authoring is often time consuming and costly. In this paper, we present a mission-based scenario modeling and generation framework for virtual training. In particular, we consider the issue of how the timing of the events in a scenario can impact the training process and how to incorporate such impact into the scenario generation. To this end, our framework is designed to explicitly capture the propagated effect of an event and its influence to other events. For representing mission-based scenarios, the concepts of mission objectives, events, and scenario beats are introduced. The generation process is designed to generate scenarios that can tailor to trainer's preference and adapt to different trainees' skill levels. The efficacy of the proposed framework is demonstrated through an empirical study of human players in a food distribution training game.
Lessons Learned From a Rational Reconstruction of Minstrel

Scott Turner's 1993 Minstrel system was a high water mark in story generation, harnessing the concept of imaginative recall to generate creative stories. Using case-based reasoning and an author level planning system, Minstrel models human creative processes. However, the algorithmic and representational commitments made in Minstrel were never subject to principled and quantitative analysis. By rationally reconstructing Minstrel, we are able to investigate Turner's computational model of creativity and learn new lessons about his architecture. We find that Minstrel's original performance was tied to a well-groomed case library, but by modifying several components of the algorithm we can create a more general version which can construct stories using a sparser and less structured case library. Through a rational reconstruction of Minstrel, we both learn new architectural and algorithmic lessons about Minstrel’s computational model of creativity as well as make his architecture available to the contemporary research community for further experimentation.
Alpha-Beta Pruning for Games with Simultaneous Moves

Alpha-Beta pruning is one of the most powerful and fundamental MiniMax search improvements. It was designed for sequential two-player zero-sum perfect information games. In this paper we introduce an Alpha-Beta-like sound pruning method for the more general class of “stacked matrix games” that allow for simultaneous moves by both players. This is accomplished by maintaining upper and lower bounds for achievable payoffs in states with simultaneous actions and dominated action pruning based on the feasibility of certain linear programs. Empirical data shows considerable savings in terms of expanded nodes compared to naive depth-first move computation without pruning.
Sequential Pattern Mining in StarCraft: Brood War for Short and Long-Term Goals

A wide variety of strategies have been used to create agents in the growing field of real-time strategy AI. However, a frequent problem is the necessity of hand-crafting competencies, which becomes prohibitively difficult in a large space with many corner cases. A preferable approach would be to learn these competencies from the wealth of expert play available. We present a system that uses the Generalized Sequential Pattern (GSP) algorithm from data mining to find common patterns in StarCraft:Brood War replays at both the micro- and macro-level, and verify that these correspond to human understandings of expert play. In the future, we hope to use these patterns to learn tasks and goals in an unsupervised manner for an HTN planner.
A hybrid content based image retrieval system using log-gabor filter banks

&lt;p&gt;In this paper, a new efficient image retrieval system using sequential process of three stages with filtering technique for the feature selection is proposed. In the first stage the color features are extracted using color histogram method and in the second stage the texture features are obtained using log-Gabor filters and in the third stage shape features are extracted using shape descriptors using polygonal fitting algorithm. The proposed log-Gabor filter in the second stage has advantages of retrieving images over regular Gabor filter for texture. It provides better representation of the images. Experimental evaluation of the proposed system shows improved performance in retrieval as compared to other existing systems in terms of average precision and average recall.&lt;/p&gt;
Toward Generating 3D Games with the Help of Commonsense Knowledge and the Crowd

Procedural game generation is the automatic creation of all aspects of a playable computer game. Procedural game generation systems require specialized knowledge, virtual worlds, and art assets. In this paper, we show how 3D graphical scenes for interactive fictions can be automatically generated with only knowledge that is readily available in existing knowledge bases or can be acquired via crowdsourcing. The key to 3D scene generation is commonly accepted spatial relationships between different types of objects in different types of scenes. We use a crowdsourcing game to automatically and rapidly acquire spatial relations. The spatial relations are used by an intelligent scene generation system that selects and configures 3D assets within a virtual geometric space.
Investigating Collaborative Creativity via Machine-Mediated Game Blending

Can the creativity of humans be enhanced through mutual cooperation, or is it a detriment to their own individual creativity? Although most artists are known for their artistic individuality, some of the best creative works were achieved through mutual collaborative efforts. This paper proposes the study of a game blending system capable of combining user- and machine-generated content from multiple users and creativity facets (e.g. audio, visuals, narrative) for the creation of complete games. Supported by mixed-initiative design tools and human computation (crowdsourcing), users create facet-specific content, while getting stimulated by other creations on different facets by other users. Our research will investigate the ability for machine input into the collaborative process to yield games of higher novelty and quality for players.
Weighted Multi-scale Local Binary Pattern Histograms for Face Recognition

This paper proposes a novel face recognition methodology, which is based on the combination of the texture operator, namely Multi-scale Local Binary Pattern (MSLBP), face image filtering and discriminative feature weighting algorithms. Presented MSLBP principle enhances the discriminative power of the original Local Binary Pattern operator. The combination of the MSLBP and low-pass filtering improves the stability of the feature vector for different scales of the input face. Proposed mini-batch discriminative feature weighting methodologies are applied to the feature space in the feature and block levels in order to enhance the components more relevant to the recognition process. The observed PCA based data compression algorithm significantly reduced the dimensionality of the feature vector with a minimal loss in the precision of the recognition process. The identification precision exceeded the threshold of 99% for the frontal subsets of a color FERET database.
Tweet Enrichment for Effective Dimensions Classification in Online Reputation Management

Online Reputation Management (ORM) is concerned with the monitoring of public opinions on social media for entities such as commercial organisations. In particular, we investigate the task of reputation dimension classification, which aims to classify tweets that mention a business entity into different dimensions (e.g. "financial performance'' or "products and services''). However, producing a general reputation dimension classification system that can be used across businesses of different types is challenging, due to the brief nature of tweets and the lack of terms in tweets that relate to specific reputation dimensions. To tackle these issues, we propose a robust and effective tweet enrichment approach that expands tweets with additional discriminative terms from a contemporary Web corpus. Using the RepLab 2014 test collection, we show that our tweet enrichment approach outperforms effective baselines including the top performing submission to RepLab 2014. Moreover, we show that the achieved accuracy scores are very close to the upper bound that our approach could achieve on this collection.
Crossing Boundaries: Multi-Level Introspection in a Complex Robotic Architecture for Automatic Performance Improvements

Introspection mechanisms are employed in agent architectures toimprove agent performance. However, there is currently no approach tointrospection that makes automatic adjustments at multiple levels inthe implemented agent system. We introduce our novel multi-levelintrospection framework that can be used to automatically adjustarchitectural configurations based on the introspection results at theagent, infrastructure and component level. We demonstrate the utilityof such adjustments in a concrete implementation on a robot where thehigh-level goal of the robot is used to automatically configure thevision system in a way that minimizes resource consumption whileimproving overall task performance.
The Generative Electronic Dance Music Algorithmic System (GEDMAS)

The Generative Electronic Dance Music Algorithmic System (GEDMAS) is a generative music system that composes full Electronic Dance Music (EDM) compositions. The compositions are based on a corpus of transcribed musical data collected through a process of detailed human transcription. This corpus data is used to analyze genre-specific characteristics associated with EDM styles. GEDMAS uses probabilistic and first order Markov chain models to generate song form structures, chord progressions, melodies and rhythms. The system is integrated with Ableton Live, and allows its user to select one or several songs from the corpus, and generate a 16 tracks/parts composition in a few clicks.
On Improving Plan Quality via Local Enhancements

There exist planning algorithms that can quickly find sub-optimal plans even for large problems and planning algorithms finding optimal plans but only for smaller problems. We attempt to integrate both approaches. We present an anytime technique for improving plan quality (decreasing the plan makespan) via substituting parts of the plan by better sub-plans. The technique guarantees optimality though it is primarily intended to quickly improve plan quality. We experimentally compare various approaches to local improvements.
Real-Time Motion Planning with Dynamic Obstacles

Robust robot motion planning in dynamic environments requires that actions be selected under real-time constraints. Existing heuristic search methods that can plan high-speed motions do not guarantee real-time performance in dynamic environments. Existing heuristic search methods for real-time planning in dynamic environments fail in the high-dimensional state space required to plan high-speed actions. In this paper, we present extensions to a leading planner for high-dimensional spaces, R*, that allow it to guarantee real-time performance, and extensions to a leading real-time planner, LSS-LRTA*, that allow it to succeed in dynamic motion planning. In an extensive empirical comparison, we show that the new methods are superior to the originals, providing new state-of-the-art search performance on this challenging problem.
Agent-Human Coordination with Communication Costs Under Uncertainty

Coordination in mixed agent-human environments is an important, yet not a simple, problem. Little attention has been given to the issues raised in teams that consist of both computerized agents and people. In such situations different considerations are in order, as people tend to make mistakes and they are affected by cognitive, social and cultural factors. In this paper we present a novel agent designed to proficiently coordinate with a human counterpart. The agent uses a neural network model that is based on a pre-existing knowledge base which allows it to achieve an efficient modeling of a human's decisions and predict their behavior. A novel communication mechanism which takes into account the expected effect of communication on the other member will allow communication costs to be minimized. In extensive simulations involving more than 200 people we investigated our approach and showed that our agent achieves better coordination when involved, compared to settings in which only humans or another state-of-the-art agent are involved.