Reviewerly/exHarmony huggingface dataset:
Lifted MEU by Weighted Model Counting

Recent work in the field of probabilistic inference demonstrated the efficiency of weighted model counting (WMC) enginesfor exact inference in propositional and, very recently, first order models. To date, these methods have not been applied to decision making models, propositional or first order, such as influence diagrams, and Markov decision networks (MDN). In this paper we show how this technique can be applied to such models. First, we show how WMC can be used to solve (propositional) MDNs. Then, we show how this can be extended to handle a first-order model — the Markov Logic Decision Network (MLDN). WMC offers two central benefits: it is a very simple and very efficient technique. This is particularly true for the first-order case, where the WMC approach is simpler conceptually, and, in many cases, more effective computationally than the existing methods for solving MLDNs via first-order variable elimination, or via propositionalization. We demonstrate the above empirically.
Towards Learned Anticipation in Complex Stochastic Environments

I describe a novel methodology by which a software agent can learn to predict future events in complex stochastic environments. It is particularly relevant to environments that are constructed specifically so as to be able to support highperformance software agents, such as video games. I present results gathered from a first prototype of our approach. The technique presented may have applications that range beyond improving agent performance, in particular to user modeling in the service of automated game testing.
The Real-Time Strategy Game Multi-Objective Build Order Problem

In this paper we examine the build order problem in real-time strategy (RTS) games in which the objective is to optimize execution of a strategy by scheduling actions with respect to a set of subgoals. We model the build order problem as a multi-objective problem (MOP), and solutions are generated utilizing a multi-objective evolutionary algorithm (MOEA). A three dimensional solution space is presented providing a depiction of a Pareto front for the build order MOP. Results of the online strategic planning tool are provided which demonstrate that our planner out-performs an expert scripted player. This is demonstrated for an AI agent in the Spring Engine Balanced Annihilation RTS game.
Automatic Story Generation for Computer Role-Playing Games

Scripting the plot in a computer role-playing game requires a large number of scripts that are difficult to track and maintain. Game adventures often have simple plots, called sub- quests, that are independent from the main plot. Sub-quests are important, as they add value to the open-world appeal of the game, but they still have to be scripted. We have created a prototype of a tool that helps by automatically producing design pattern specifications for sub-quests. The specifications can be entered into an existing tool, called ScriptEase, to generate scripting code for Neverwinter Nights adventures, without doing any manual scripting. The sub-quest patterns produced are logically consistent, ensuring the story can be completed by the player. The sub-quests are also designed to produce a better story by having the author adjust the amount of interactivity between the sub-quests. The entire process is done with little input from the author.
The Causal Relationship Between Institutions and Economic Growth: An Empirical Investigation for Pakistan Economy

This paper investigates relationship between institutional quality and economic performance in Pakistan using the Johansen-Juselius cointegration technique and the Granger causality test. The study results indicate that Institutions and growth are cointegrated and thus exhibit a reliable long run relationship. The Granger causality test findings indicate that the causality between Institutions and growth is uni-directional.However, there is no short run causality from Institutions to growth and vice versa. Therefore, as a policy implication that institutional quality may cause to the sustainable increase in country’s income in the long run, and success of any policy could be influenced by the soundness of institutions.
Sports Commentary Recommendation System (SCoReS): Machine Learning for Automated Narrative

Automated sports commentary is a form of automated narrative. Sports commentary exists to keep the viewer informed and entertained. One way to entertain the viewer is by telling brief stories relevant to the game in progress. We introduce a system called the Sports Commentary Recommendation System (SCoReS) that can automatically suggest stories for commentators to tell during games. Through several user studies, we compared commentary using SCoReS to three other types of commentary and show that SCoReS adds significantly to the broadcast across several enjoyment metrics. We also collected interview data from professional sports commentators who positively evaluated a demonstration of the system. We conclude that SCoReS can be a useful broadcast tool, effective at selecting stories that add to the enjoyment and watchability of sports. SCoReS is a step toward automating sports commentary and, thus, automating narrative.
EXPANDING DEMAND FOR ONLINE HIGHER EDUCATION: SURVEYING PROSPECTIVE STUDENTS

The Eduventures survey examined next-generation demand for online postsecondary education, assessing online experience, delivery mode and marketing channels preferences, and perceptions of price, quality and location, identifying key takeaways in each area
Glengarry Glen Ross: Using BDI for Sales Game Dialogues

Serious games offer an opportunity for players to learn communication skills by practicing conversations with nonplaying characters (NPCs). To realize this potential, the player needs freedom of play to discover the relationships between its actions and their effects on the partner and the conversation. Scripting is currently the common approach to design in-game dialogue. Although scripting is a robust technique, the approach tends to produce deterministic conversations, allowing little control to the player. It is claimed that a Belief-Desire-Intention (BDI) approach to model the behavior of NPCs allows greater freedom to the player, and delivers better scalability and re-use of dialogues. This claim is evaluated by using BDI in the development of a sales-talk training game in the real-estate domain. It is concluded that BDI enables representative NPCs that respond appropriately and the game allows the player its freedom of choice to explore. The results also showed that BDI brings about new challenges to address, in order to further increase the quality of in-game dialogue.
Linguistic Bias in Collaboratively Produced Biographies: Crowdsourcing Social Stereotypes?

Language is the primary medium through which stereotypes are conveyed. Even when we avoid using derogatory language, there are many subtle ways in which stereotypes are created and reinforced, and they often go unnoticed. Linguistic bias, the systematic asymmetry in language patterns as a function of the social group of the persons described, may play a key role. We ground our study in the social psychology literature on linguistic biases, and consider two ways in which biases might manifest: through the use of more abstract versus concrete language, and subjective words. We analyze biographies of African American and Caucasian actors at the Internet Movie Database (IMDb), hypothesizing that language patterns vary as a function of race and gender. We find that both attributes are correlated to the use of abstract, subjective language. Theory predicts that we describe people and scenes that are expected, as well as positive aspects of our in-group members, with more abstract language. Indeed, white actors are described with more abstract, subjective language at IMDb, as compared to other social groups. Abstract language is powerful because it implies stability over time; studies have shown that people have better impressions of others described in abstract terms. Therefore, the widespread prevalence of linguistic biases in social media stands to reinforce social stereotypes. Further work should consider the technical and social characteristics of the collaborative writing process that lead to an increase or decrease in linguistic biases.
Generating Believable Stories in Large Domains

Planning-based techniques are a very powerful tool for automated story generation. However, as the number of possible actions increases, traditional planning techniques suffer from a combinatorial explosion due to large branching factors. In this work, we apply Monte Carlo Tree Search (MCTS) techniques to generate stories in domains with large numbers of possible actions (100+). Our approach employs a Bayesian story evaluation method to guide the planning towards believable stories that reach a user defined goal. We generate stories in a novel domain with different type of story goals. Our approach shows an order of magnitude improvement in performance over traditional search techniques.
Network Sampling Designs for Relational Classification

Relational classification has been extensively studied recently due to its applications in social, biological, technological, and information networks. Much of the work in relational learning has focused on analyzing input data that comprise a single network. Although machine learning researchers have considered the issue of how to sample training and test sets from the input network (for evaluation), the mechanisms which are used to construct the input networks have largely been ignored. In most cases, the input network has itself been sampled from a larger target network (e.g., Facebook) and often the researcher is unaware of how the input network was constructed or what impact that may have on evaluation of the relational models. Since the goal in evaluating relational classification algorithms is to accurately assess their performance on the larger target network, it is critical to understand what impact the initial sampling method may have on our estimates of classification accuracy.In this paper, we present different sampling methods and systematically study their impact on evaluation of relational classification. Our results indicate that the choice of sampling method can impact classification performance, and thus consequently affects the accuracy of evaluation.
Modular Computational Critics for Games

Formal game modeling tools could support the automated analysis of game rules and rapid automated play-testing, but are not widely used. Furthermore, existing game design support tools are often limited to very specific classes of game, require significant programming expertise to use or customize, or are fully-automatic tools with limited affordances for human designers. We therefore propose a framework for authoring computational game design critics and a new game definition language (Gamelan) grounded in the conventions of board game rules. We show how a set of these critics could have detected specific, attested design problems in the development of Donald X. Vaccarino's influential card game Dominion. We also illustrate an extension of this approach to other collectible card games, turn-taking games, and games in general.
Gameplay as On-Line Mediation Search

Mediation is a plan-based interactive narrative generation algorithm that creates a cascading policy of plans. This policy represents a branching story and can be used by an execution manager in a game to control the series of events that unfold. With a few modifications we show that mediation's search space can embed all possible traversals through a game world. This shift allows gameplay to be modeled as an on-line mediation search where the game's interface is a representation of the underlying graph traversal. In this paper we outline these modifications and present a text-based implementation.
Using Game Reviews to Recommend Games

We present a recommender system intended to be used by a community of gamers. The system uses free-form text reviews of games written by the members of the community, along with information about the games that a particular user likes, in order to recommend new games that are likely to be of interest to that user. The system uses the frequency of co-occurrence of word pairs that appear in the reviews of a game as features that represent the game. The pairs consist of adjectives and context words; i.e., words that appear close to an adjective in a review. Because of the extremely large number of possible combinations of adjectives and context words, we use information-theoretic co-clustering of the adjective-context word pairs to reduce the dimensionality. Games are represented using the standard information retrieval vector space model, in which vector features are based on the frequency of occurrence of cocluster pairs.We present the results of three experiments with our system. In the first experiment, we use a variety of strategies to relate frequencies of co-cluster pairs to vector features, to see which produces the most accurate recommendations. In the second, we explore the effects of co-cluster dimensionality on the quality of our system’s recommendations. In the third experiment, we compare our approach to a baseline approach using a bag-of-words technique and conclude that our approach produces higher quality recommendations.
Automating Direct Speech Variations in Stories and Games

Dialogue authoring in large games requires not only content creation but the subtlety of its delivery, which can vary from character to character. Manually authoring this dialogue can be tedious, time-consuming, or even altogether infeasible. This paper utilizes a rich narrative representation for modeling dialogue and an expressive natural language generation engine for realizing it, and expands upon a translation tool that bridges the two. We add functionality to the translator to allow direct speech to be modeled by the narrative representation, whereas the original translator supports only narratives told by a third person narrator. We show that we can perform character substitution in dialogues. We implement and evaluate a potential application to dialogue implementation: generating dialogue for games with big, dynamic, or procedurally-generated open worlds. We present a pilot study on human perceptions of the personalities of characters using direct speech, assuming unknown personality types at the time of authoring.
A First-Order Interpreter for Knowledge-Based Golog with Sensing based on Exact Progression and Limited Reasoning

While founded on the situation calculus, current implementations of Golog are mainly based on the closed-world assumption or its dynamic versions or the domain closure assumption. Also, they are almost exclusively based on regression. In this paper, we propose a first-order interpreter for knowledge-based Golog with sensing based on exact progression and limited reasoning. We assume infinitely many unique names and handle first-order disjunctive information in the form of the so-called proper+ KBs. Our implementation is based on the progression and limited reasoning algorithms for proper+ KBs proposed by Liu, Lakemeyer and Levesque. To improve efficiency, we implement the two algorithms by grounding via a trick based on the unique name assumption. The interpreter is online but the programmer can use two operators to specify offline execution for parts of programs. The search operator returns a conditional plan, while the planning operator is used when local closed-world information is available and calls a modern planner to generate a sequence of actions.
A SAT-Based Approach to Cooperative Path-Finding Using All-Different Constraints

The approach to solving cooperative-path finding (CPF) as satisfiability (SAT) is revisited. An alternative encoding that exploits multi-valued state variables representing locations where a given agent resides is suggested. This encoding employs the ALL-DIFFERENT constraint to model the requirement that agents must not collide with each other. We show that our new domain-dependent encoding enables finding of optimal or near optimal solutions to CPFs in certain hard setups where A*-based techniques such as WHCA* fail to do so. Our finding is also that the ALL-DIFFERENT encoding can be solved faster than the existent encoding.
“Dancing with the Stars,” NBA Games, Politics: An Exploration of Twitter Users’ Response to Events

Microblogging services such as Twitter offer great opportunities for analyzing the reactions of a wide audience with respect to current events. In this paper, we explore the correlation between types of user engagement and events centered around celebrities (e.g., personal or professional events involving Actors, Musicians, Politicians, Athletes).
Are We There Yet? — Estimating Search Progress

Heuristic search is a general problem solving technique. While most evaluations of heuristic search focus on the speed of search, there are relatively few techniques for predicting when search will end. This paper provides a study of progress estimating techniques for optimal, suboptimal, and bounded suboptimal heuristic search algorithms. We examine two previously proposed techniques, search velocity and search vacillation, as well as two new approaches, path-based estimation and distribution-based estimation. We find that both new approaches are better at estimating the remaining amount of search effort than previous work in all three varieties of search, occasionally erring by less than 5%.
Efficient Single Frontier Bidirectional Search

The Single Frontier Bi-Directional Search (SBS) framework was recently introduced. A node in SBS corresponds to a pair of states, one from each of the frontiers and it uses front-to- front heuristics. In this paper we present an enhanced version of SBS, called eSBS, where pruning and caching techniques are applied, which significantly reduce both time and memory needs of SBS. We then present a hybrid of eSBS and IDA∗ which potentially uses only the square root of the memory required by A∗ but enables to prune many nodes that IDA∗ would generate. Experimental results show the benefit of our new approaches on a number of domains.
Subdimensional Expansion and Optimal Task Reassignment

Multirobot path planning and task assignment are traditionally treated separately, however task assignment can greatly impact the difficulty of the path planning problem, and the ultimate quality of solution is dependent upon both. We introduce task reassignment, an approach to optimally solving the coupled task assignment and path planning problems. We show that task reassignment improves solution quality, and reduces planning time in some situations.
Chatting On-line: An Assessment of Bilingualism and The Social Contexts of Language in Lebanon

This study aims to explore the Lebanese sociolinguistics as manifested in chatting, that is, to assess how a sample of Lebanese students use their languages skills while carrying an on-line conversation via chatting. The research will also investigate the variations governed by sociolinguistics branding the Lebanese chatting community. To carry out this study, a mix approach of quantitative and qualitative methodology is used. Indeed, the research will describe the participants’ computer literacy and how this is interrelated to the choice of chatting language. The researcher uses two methods, formal interviews with selected chatters and a survey questionnaire that reflects both linguistic issues and computer and chatting literacy. Data analysis uses SPSS version 25 software and performed descriptively. Findings show that although having a multilingual society is considered a positive and uplifting certitude, this fact should not be taken for granted and languages must be directed properly. Students cannot be left without guidance to the use of any language.
Manchester Metropolitan at SemEval-2021 Task 1: Convolutional Networks for Complex Word Identification

We present two convolutional neural networks for predicting the complexity of words and phrases in context on a continuous scale.Both models utilize word and character embeddings alongside lexical features as inputs.Our system displays reasonable results with a Pearson correlation of 0.7754 on the task as a whole.We highlight the limitations of this method in properly assessing the context of the target text, and explore the effectiveness of both systems across a range of genres.Both models were submitted as part of LCP 2021, which focuses on the identification of complex words and phrases as a context dependent, regression based task.
TUDA-CCL at SemEval-2021 Task 1: Using Gradient-boosted Regression Tree Ensembles Trained on a Heterogeneous Feature Set for Predicting Lexical Complexity

In this paper, we present our systems submitted to SemEval-2021 Task 1 on lexical complexity prediction (Shardlow et al., 2021a).The aim of this shared task was to create systems able to predict the lexical complexity of word tokens and bigram multiword expressions within a given sentence context, a continuous value indicating the difficulty in understanding a respective utterance.Our approach relies on gradient boosted regression tree ensembles fitted using a heterogeneous feature set combining linguistic features, static and contextualized word embeddings, psycholinguistic norm lexica, WordNet, word-and character bigram frequencies and inclusion in word lists to create a model able to assign a word or multiword expression a context-dependent complexity score.We can show that especially contextualised string embeddings (Akbik et al., 2018) can help with predicting lexical complexity.
Improvement of Chacha20 Algorithm based on Tent and Chebyshev Chaotic Maps

Chacha 20 is a stream cypher that is used as lightweight on many CPUs that ‎do not have dedicated AES instructions. As stated by Google, that is the reason why they use it on many ‎devices, such as mobile devices, for authentication in TLS protocol. This paper ‎proposes an improvement of chaha20 stream cypher algorithm based on tent and ‎Chebyshev functions (IChacha20). The main objectives of the proposed IChacha20 ‎algorithm are increasing security layer, designing a robust structure of the IChacha20 ‎to be enabled to resist various types of attacks, implementing the proposed ‎algorithm for encryption of colour images, and transiting it in a secure manner. The ‎ test results proved that the MSE, PSNR, UQI and NCC metrics of ‎IChacha20 are better than those of the original Chacha20. Also, the proposed method has ‎a faster execution time (01:26:4 sec) compared with the original algorithm (02:07:1 sec).