The prototyping of computer games, particularly card games, requires extensive human effort in creative ideation and gameplay evaluation.Recent advances in Large Language Models (LLMs) offer opportunities to automate and streamline these processes.However, it remains challenging for LLMs to design novel game mechanics beyond existing databases, generate consistent gameplay environments, and develop scalable gameplay AI for large-scale evaluations.This paper addresses these challenges by introducing a comprehensive automated card game prototyping framework.The approach highlights a graph-based indexing method for generating novel game variations, an LLM-driven system for consistent game code generation validated by gameplay records, and a gameplay AI constructing method that uses an ensemble of LLM-generated heuristic functions optimized through self-play.These contributions aim to accelerate card game prototyping, reduce human labor, and lower barriers to entry for game developers.

Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks and beyond. This success of LLMs has led to a large influx of research contributions in this direction. These works encompass diverse topics such as architectural innovations, better training strategies, context length improvements, fine-tuning, multimodal LLMs, robotics, datasets, benchmarking, efficiency, and more. With the rapid development of techniques and regular breakthroughs in LLM research, it has become considerably challenging to perceive the bigger picture of the advances in this direction. Considering the rapidly emerging plethora of literature on LLMs, it is imperative that the research community is able to benefit from a concise yet comprehensive overview of the recent developments in this field. This article provides an overview of the literature on a broad range of LLM-related concepts. Our self-contained comprehensive overview of LLMs discusses relevant background concepts along with covering the advanced topics at the frontier of research in LLMs. This review article is intended to provide not only a systematic survey but also a quick, comprehensive reference for the researchers and practitioners to draw insights from extensive, informative summaries of the existing works to advance the LLM research.

Previous studies regarding the perception of emotions for embodied virtual agents have shown the effectiveness of using virtual characters in conveying emotions through interactions with humans. However, creating an autonomous embodied conversational agent with expressive behaviors presents two major challenges. The first challenge is the difficulty of synthesizing the conversational behaviors for each modality that are as expressive as real human behaviors. The second challenge is that the affects are modeled independently, which makes it difficult to generate multimodal responses with consistent emotions across all modalities. In this work, we propose a conceptual framework, ACTOR (Affect-Consistent mulTimodal behaviOR generation), that aims to increase the perception of affects by generating multimodal behaviors conditioned on a consistent driving affect. We have conducted a user study with 199 participants to assess how the average person judges the affects perceived from multimodal behaviors that are consistent and inconsistent with respect to a driving affect. The result shows that among all model conditions, our affect-consistent framework receives the highest Likert scores for the perception of driving affects. Our statistical analysis suggests that making a modality affect-inconsistent significantly decreases the perception of driving affects. We also observe that multimodal behaviors conditioned on consistent affects are more expressive compared to behaviors with inconsistent affects. Therefore, we conclude that multimodal emotion conditioning and affect consistency are vital to enhancing the perception of affects for embodied conversational agents.

The visualization of spaces, both virtual and built, has long been an important part of the environment design process. Industry tools to visualize occupancy have grown from simple drop-in stock photos post-design to real-time crowds simulations. However, while treatment of visualization and collaborative design processes has long been discussed in the HCI and Architecture communities, these inclusive design methods are infrequently seen in architecture education (e.g. studio) and practice, nor implemented in licensure requirements – leaving designers to think about the future occupants on their own. While there are strong indicators of the impact visualization modality and rendering style have on perception of scale and space, little has been explored regarding how we represent the human form with respect to these design tools and practices. We present findings from a novel online interactive space planning and estimation study that examines the effects of 3 common building visualization modalities in the design process with 3 human form modalities extracted from the architecture literature. Results indicate the type of visualization changes the number of occupants estimated, and that designers prefer integrated manikins within building models when estimating space usage, although their acceptance was equally divided between 2D and 3D. Our findings lay the foundation for new and focused design tools integrating human form and factors at building scale.

In recent years, human trajectory prediction (HTP) has garnered attention in computer vision literature. Although this task has much in common with the longstanding task of crowd simulation, there is little from crowd simulation that has been borrowed, especially in terms of evaluation protocols. The key difference between the two tasks is that HTP is concerned with forecasting multiple steps at a time and capturing the multimodality of real human trajectories. A majority of HTP models are trained on the same few datasets, which feature small, transient interactions between real people and little to no interaction between people and the environment. Unsurprisingly, when tested on crowd egress scenarios, these models produce erroneous trajectories that accelerate too quickly and collide too frequently, but the metrics used in HTP literature cannot convey these particular issues. To address these challenges, we propose (1) the A2X dataset, which has simulated crowd egress and complex navigation scenarios that compensate for the lack of agent-to-environment interaction in existing real datasets, and (2) evaluation metrics that convey model performance with more reliability and nuance. A subset of these metrics are novel multiverse metrics, which are better-suited for multimodal models than existing metrics. The dataset is available at: https://mubbasir.github.io/HTP-benchmark/.

Abstract At present, environment designers mostly use their intuition and experience to predictively account for how environments might support dynamic activity. The majority of Computer‐Aided Design tools only provide a static representation of space which potentially ignores the impact that an environment layout produces on its occupants and their movements. To address this, computational techniques such as crowd simulation have been developed. With few exceptions, crowd simulation frameworks are often decoupled from environment modeling tools. They usually require specific hardware/software infrastructures and expertise to be used, hindering the designers' abilities to seamlessly simulate, analyze, and incorporate movement‐centric dynamics into their design workflows. To bridge this disconnect, we devise a cross‐browser service‐based simulation analytics platform to analyze environment layouts with respect to occupancy and activity. Our platform allows users to access simulation services by uploading three‐dimensional environment models in numerous common formats, devise targeted simulation scenarios, run simulations, and instantly generate crowd‐based analytics for their designs. We conducted a case study to showcase cross‐domain applicability of our service‐based platform, and a user study to evaluate the usability of this approach.

In architectural design, architects explore a vast amount of design options to maximize various performance criteria, while adhering to specific constraints. In an effort to assist architects in such a complex endeavour, we propose IDOME, an interactive system for computer-aided design optimization. Our approach balances automation and control by efficiently exploring, analyzing, and filtering space layouts to inform architects' decision-making better. At each design iteration, IDOME provides a set of alternative building layouts which satisfy user-defined constraints and optimality criteria concerning a user-defined space parametrization. When the user selects a design generated by IDOME, the system performs a similar optimization process with the same (or different) parameters and objectives. A user may iterate this exploration process as many times as needed. In this work, we focus on optimizing built environments using architectural metrics by improving the degree of visibility, accessibility, and information gaining for navigating a proposed space. This approach, however, can be extended to support other kinds of analysis as well. We demonstrate the capabilities of IDOME through a series of examples, performance analysis, user studies, and a usability test. The results indicate that IDOME successfully optimizes the proposed designs concerning the chosen metrics and offers a satisfactory experience for users with minimal training.

The importance of social distancing for public health is well established. However, the policies and regulations regarding occupancy rates have not been designed with this in mind. While there are analytical tools and related measures that are used in practice to evaluate how the design of a built environment serves the needs of its intended occupants, these metrics cannot directly apply to the problem of preventing the spread of infectious diseases such as COVID-19. By using a crowd-based simulator using three levels of behavior and agent control in a given environment, a novel evaluation metric for a space layout can be calculated to reflect the proclivity of maintaining a safe distance throughout the shopping experience. We refer to this metric as the Social Distancing Index (SDI), accounting for the occupancy throughput and number of distance-based violations found. Through a case study of a realistic retail store, we demonstrate the proposed platforms performance and output on multiple scenarios by changing agent-behavior, occupancy rate, and navigational guidelines.

Crowd simulations provide a practical approach to evaluate building design alternatives with respect to human-centric criteria, such as evacuation times and flow in case of emergency scenarios. Coupled with Building Information Modeling (BIM) tools, they support architects’ iterative exploration of design alternatives. However, methods based on manually configuring a design and a corresponding simulation are not practical for exploring the potentially very large number of design solutions that satisfy human-centric design goals and requirements. Often, for practical reasons, designers may consider standard crowd configurations which do not capture the behavior of diverse occupants that may exhibit different locomotion abilities, movement patterns, and social behaviors. We posit that a joint exploration of high-dimensional building design and occupancy features is necessary to more accurately capture the mutual relations between buildings and the behavior of their occupants. To test this hypothesis, we conducted a series of experiments to automatically explore joint high dimensional design–occupancy patterns using an unsupervised pattern recognition technique (i.e. K-MEANS). We demonstrate that joint design–occupancy explorations provide more accurate results compared with sequential exploration processes that consider default design or crowd features, despite the longer computational times to simulate a large number of solutions. The findings of this case study have practical applications to the design of next-generation design exploration tools that support human-centric analyses in architectural design.

We present a computational spatial analytics tool for designing environments that better support human-related factors. Our system performs both static and dynamic analyses: the first relates to the building geometry and organization, while the second additionally considers the crowd movement in the space. The results are presented to the designers in the form of numerical values, traces and heat maps displayed on top of the floor plan. We demonstrate our approach with a user study whereby novice architects have tested the proposed approach to iteratively improve a building accessibility in real-time with respect to a selected number of static and dynamic metrics. The results indicate that the users were able to successfully improve their design solutions and thus generate more human-aware environments. The usability and effectiveness of the tool where also measured, yielding positive scores. The modular and flexible nature of the tool enables further extension to incorporate additional static and dynamic spatial metrics.

Floor plan designs and their spatial analysis are typically constrained to blueprints and 2D projections of 3D models. Computing appropriate spatial measures from such representations provides a standard way of quantifying important aspects of the design. We wish to investigate whether a person's perceptual exploration of a space would agree with such spatial measures, that is, whether a person can roughly infer such measures by exploring a space. We perform two studies, one involving novices and the other experts. First, we conduct a perceptual study to discover whether a novice user's perception of spatial measures depends on the mode used to explore the space. Our analysis considers three spatial measures, grounded in Space-Syntax, that characterize key aspects of a design such as visibility, accessibility, and organization. We compare three modes of exploration: 2D blueprints, first-person view in a 3D simulation, and a 3D virtual reality simulation with teleportation. A correlation analysis between the users' perceptual ratings and the spatial measures, indicates that virtual reality is the most effective of the three methods, while 2D blueprints and 3D first-person exploration often fail entirely to convey the spatial measures. In the second study, experts are asked to evaluate and rank the design blueprints for each measure. The expert observations are in strong agreement with the spatial measures for accessibility and organization, but not for visibility in some cases. This indicates that even experts have difficulty understanding spatial aspects of an architecture design from 2D blueprints alone.

Crowd-aware environment design is a complex combinatorial decision process, where small changes in a design may affect crowd flow patterns in unexpected and potentially unintuitive ways. Existing solutions rely on expert intuition, best practices, or automation. To address the dimensionality and complexity of the design process, we propose leveraging automation and human creativity at a large scale akin to crowd sourcing, within a gamified collaborative design framework. Using our system, "players" (novice users or experts) can rapidly iterate on their designs while soliciting feedback from computer simulations of crowd movement and the designs of other players. Our approach affords a new way of thinking of the solution space in that it inherently supports competitive collaboration, co-design, and crowd sourced solutions. We evaluate our framework through a preliminary user study.

Abstract Traffic and pedestrian dynamics communities often use a standard qualitative classification, namely, level of service (LoS), to describe the relationship between the crowd flow and crowd density in an environment. However, this classification has not yet been rigorously studied in the application of synthetic crowds, which are derived using a variety of approaches and may model certain behaviors better than others. Although synthetic crowds can be simulated to extrapolate crowd flow for rigorous quantitative analysis, these may be at odds with the qualitative LoS. In order to successfully use computer‐assisted design, it is important to have sound quantitative metrics as the basis for analysis and optimization. In this paper, we present a systematic empirical analysis of LoS for synthetic crowds. Using established crowd simulation techniques, we quantify the relation between crowd density and crowd flow for evacuation scenarios across different simulators to explore conformity to qualitative LoS classifications. Following this study, we perform environment optimization experiments under various LoS conditions. Finally, we test the generality of optimizing under these LoS conditions. Our results motivate the need for further study, using real and synthetic crowd datasets across representative environment benchmarks.

Abstract We present crowd‐optimized design of environments (CODE): a “crowd‐aware” computational tool for designing environments (e.g., building floor plans). Our system analyses the impact of newly added environment elements (e.g., pillars or doorways) on the resulting crowd flow, using current‐generation crowd simulators. The results of the simulation are used to provide feedback to the designer in terms of aggregate statistics and heat maps. Additionally, our system is able to “automatically” optimize the placement of environment elements to maximize crowd flow in egress scenarios, while satisfying constraints that are imposed by the designer. Using CODE, architects and environment designers can iteratively refine upon their original design to quickly accommodate the dynamic properties of crowd simulations in an interactive fashion. CODE is modular and flexible so that designers may build environments, select from different crowd simulators, and specify varying crowd configurations.

Level of service (LoS) is a standard indicator, widely used in crowd management and urban design, for characterizing the service afforded by environments to crowds of specific densities. However, current LoS indicators are qualitative and rely on expert analysis. Computational approaches for crowd analysis and environment design require robust measures for characterizing the relationship between environments and crowd flow.

Abstract The layout of a building, real or virtual, affects the flow patterns of its intended users. It is well established, for example, that the placement of pillars at proper locations can often facilitate pedestrian flow during the evacuation of a building. Such considerations are therefore important for architects, game level developers, and others whose domains involve agents navigating through buildings. In this paper, we take the first steps towards developing a simulation framework that can be used to study the optimal placement of architectural elements, such as pillars or doors, for the purposes of facilitating dense pedestrian flow during the evacuation of a building. In particular, we show that the steering algorithms used to model the local navigation abilities of the agents significantly affect the results, which motivates the need for a statistically valid approach and further study. Copyright © 2015 John Wiley & Sons, Ltd.