Amorphous Fortress: Observing Emergent Behavior in Multi-Agent FSMs

We introduce a system called Amorphous Fortress -- an abstract, yet spatial, open-ended artificial life simulation. In this environment, the agents are represented as finite-state machines (FSMs) which allow for multi-agent interaction within a constrained space. These agents are created by randomly generating and evolving the FSMs; sampling from pre-defined states and transitions. This environment was designed to explore the emergent AI behaviors found implicitly in simulation games such as Dwarf Fortress or The Sims. We apply the hill-climber evolutionary search algorithm to this environment to explore the various levels of depth and interaction from the generated FSMs.Comment: 9 pages; Accepted to the 1st ALIFE for and from video games Workshop 202

Say "Sul Sul!" to SimSim, A Sims-Inspired Platform for Sandbox Game AI

This paper proposes environment design in the life simulation game The Sims as a novel platform and challenge for testing divergent search algorithms. In this domain, which includes a minimal viability criterion, the goal is to furnish a house with objects that satisfy the physical needs of a simulated agent. Importantly, the large number of objects available to the player (whether human or automated) affords a wide variety of solutions to the underlying design problem. Empirical studies in a novel open source simulator called SimSim investigate the ability of novelty-based evolutionary algorithms to effectively generate viable environment designs.Comment: 7 pages, Accepted as poster to AIIDE 202

The Five-Dollar Model: Generating Game Maps and Sprites from Sentence Embeddings

The five-dollar model is a lightweight text-to-image generative architecture that generates low dimensional images from an encoded text prompt. This model can successfully generate accurate and aesthetically pleasing content in low dimensional domains, with limited amounts of training data. Despite the small size of both the model and datasets, the generated images are still able to maintain the encoded semantic meaning of the textual prompt. We apply this model to three small datasets: pixel art video game maps, video game sprite images, and down-scaled emoji images and apply novel augmentation strategies to improve the performance of our model on these limited datasets. We evaluate our models performance using cosine similarity score between text-image pairs generated by the CLIP VIT-B/32 model.Comment: to be published in AIIDE 202

The Five-Dollar Model: Generating Game Maps and Sprites from Sentence Embeddings

The five-dollar model is a lightweight text-to-image generative architecture that generates low dimensional images or tile maps from an encoded text prompt. This model can successfully generate accurate and aesthetically pleasing content in low dimensional domains, with limited amounts of training data. Despite the small size of both the model and datasets, the generated images or maps are still able to maintain the encoded semantic meaning of the textual prompt. We apply this model to three small datasets: pixel art video game maps, video game sprite images, and down-scaled emoji images and apply novel augmentation strategies to improve the performance of our model on these limited datasets. We evaluate our models' performance using cosine similarity score between text-image pairs generated by the CLIP VIT-B/32 model to demonstrate quality generation

Efficacy and safety of combined spinal: Epidural versus epidural technique for labor analgesia in parturients with rheumatic valvular heart disease

Background: Hemodynamic changes induced by labor pain and apprehension in addition to physiological changes may pose risk to parturients with rheumatic heart disease (RHD). Therefore, it is important to provide adequate pain relief during labor in these patients. We planned this study to compare the efficacy and safety of epidural (E) versus combined spinal - epidural (CSE) for labor analgesia in parturients with rheumatic valvular heart disease. Methods: Twenty-five parturients with RHD included in this study were randomized to one of the two groups - E group (n = 12), received 6 mL of 0.0625% bupivacaine with 25 μg fentanyl or CSE group (n = 13), received 25 μg of fentanyl with 1.25 mg bupivacaine diluted to 1 mL in subarachnoid space. Afterward, a continuous infusion of 0.1% bupivacaine with 2 μg/mL fentanyl was started at 6-8 mL/h. Primary outcome, analgesic efficacy, was assessed by visual analog scale (VAS) for pain. VAS ≤ 3 was considered as effective analgesia. Rescue analgesia in the form of epidural bolus was given if VAS > 3. Results: Demographic characteristics of the patients were comparable. Mitral stenosis was the predominant valvular lesion. The VAS at which the parturients received analgesia was comparable. The mean time to achieve effective analgesia was significantly faster in CSE group (4.46 ± 0.87 min) compared with group E (15.09 ± 5.7 min) (P < 0.001). Significantly lower median pain scores were recorded until the initial 15 min in CSE group. Afterward, median VAS for pain was comparable between the groups. VAS for pain was significantly low at all time intervals than baseline in both the groups. Maternal satisfaction and incidence of cesarean rate and complication were comparable between the groups. Conclusion: Both epidural and CSE are equally effective and safe for labor analgesia in parturients with rheumatic valvular heart disease. However, CSE technique provides a faster onset of analgesia