An experimental evaluation of domain-independent fault handling services open multi-agent systems

Title from cover. "May 2000."Includes bibliographical references (p. 13-16).Supported in part by NSF. IIS-9803251 Supported in part by DARPA. F30602-98-2-0099Chrysanthos Dellarocas and Mark Klein

Level Generation Through Large Language Models

Large Language Models (LLMs) are powerful tools, capable of leveraging their training on natural language to write stories, generate code, and answer questions. But can they generate functional video game levels? Game levels, with their complex functional constraints and spatial relationships in more than one dimension, are very different from the kinds of data an LLM typically sees during training. Datasets of game levels are also hard to come by, potentially taxing the abilities of these data-hungry models. We investigate the use of LLMs to generate levels for the game Sokoban, finding that LLMs are indeed capable of doing so, and that their performance scales dramatically with dataset size. We also perform preliminary experiments on controlling LLM level generators and discuss promising areas for future work

Domain-independent exception handling services that increase robustness in open multi-agent systems

Title from cover. "May 2000."Includes bibliographical references (p. 17-23).Mark Klein and Chrysanthos Dellarocas

Computer Aided Content Generation:A Gloomhaven Case Study

We present how an evolutionary algorithm can be used to generate scenarios for the board game Gloomhaven. The scenarios are evaluated according to size, difficulty, thematic coherence, complexity and layout. We encode the game's default scenarios into textual descriptions and use them as initial population for the algorithm. Our dungeon generation works within the confines given by the physical board game, i.e., special attention is given to availability of game pieces and map tiles. The generated dungeons can be constructed without overlapping tiles.</p

Automatic puzzle level generation : a general approach using a description language

In this paper, we present a general technique to generate and evaluate puzzle levels made by Puzzle Script. Puzzle Script is a videogame description language created by Stephen Lavelle for scripting puzzle games. We propose a system to help in generating levels for Puzzle Script without any restriction on the current game rules. Two different approaches are used with a trade off between speed (Constructive approach) and playability (Genetic approach). These two approaches use a level evaluator that calculates the scores of the generated levels based on their playability and challenge. The generated levels are assessed by human players statistically, and the results show that the constructive approach is capable of generating playable levels up to 90%, while genetic approach can reach up to 100%. The results also show a high correlation between the system scores and the human scores.peer-reviewe

Strategic Issues, Problems and Challenges in Inductive Theorem Proving

Abstract(Automated) Inductive Theorem Proving (ITP) is a challenging field in automated reasoning and theorem proving. Typically, (Automated) Theorem Proving (TP) refers to methods, techniques and tools for automatically proving general (most often first-order) theorems. Nowadays, the field of TP has reached a certain degree of maturity and powerful TP systems are widely available and used. The situation with ITP is strikingly different, in the sense that proving inductive theorems in an essentially automatic way still is a very challenging task, even for the most advanced existing ITP systems. Both in general TP and in ITP, strategies for guiding the proof search process are of fundamental importance, in automated as well as in interactive or mixed settings. In the paper we will analyze and discuss the most important strategic and proof search issues in ITP, compare ITP with TP, and argue why ITP is in a sense much more challenging. More generally, we will systematically isolate, investigate and classify the main problems and challenges in ITP w.r.t. automation, on different levels and from different points of views. Finally, based on this analysis we will present some theses about the state of the art in the field, possible criteria for what could be considered as substantial progress, and promising lines of research for the future, towards (more) automated ITP

Literature review of procedural content generation in puzzle games

This is the third chapter from my Master Thesis (Automatic Game Generation). This chapter will provide a review of the past work on Procedural Content Generation. It highlights different efforts towards generating levels and rules for games. These efforts are grouped according to their similarity and sorted chronologically within each group.N/

A study of the importance of cultural factors in the user interaction with, and the design of, interactive science and technology exhibits in museums

This research investigates the cultural factors affecting the use of interactive science exhibits including interactive science and technology exhibits (ISTEs) by visitors to science museums worldwide. Visitors bring differing characteristics and experiences to bear upon the task of using these exhibits. These affect the nature and quality of their interaction with the exhibits. This research has focused on the cultural issues, and has defined them using 10 distinct and coherent ‘dimensions’. This has been achieved by extensive review of relevant earlier research work and building on this with experimental studies with visitors and interviews with science museum experts in the UK and Thailand. Interactive science exhibits now take many forms, and therefore for scientific investigation of their use it is essential to classify them in a form which promotes research validity and reliability. This research has developed a new classification of interactive science exhibits into four classes based upon the user’s perception, cognition and the nature of the interaction. The classes are: (1) simple interaction with direct understanding; (2) simple interaction with complex understanding; (3) multiple interactions with direct understanding; and (4) multiple interactions with complex understanding. This classification was used in experimental studies of interaction with exhibits at science museums. The research methods used mixed methods of quantitative and qualitative research through three separate studies. The data collection methods were: interviews, questionnaires, and video recording observation. The findings were that not only language issues and conceptual understanding are important factors, but other cultural factors were also inter-related and affect visitors’ learning through ISTEs