To Simsim, a Sims-Inspired Platform for Sandbox Game AI
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Proceedings of the Sixteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-20) Say “Sul Sul!∗” to SimSim, A Sims-Inspired Platform for Sandbox Game AI Megan Charity Dipika Rajesh Rachel Ombok L. B. Soros Tandon School of Engineering Tandon School of Engineering Tandon School of Engineering Tandon School of Engineering New York University New York University New York University New York University [email protected] [email protected] [email protected] [email protected] Abstract mary player task is then to select actions that will prevent the Sim’s needs from becoming critically low. Importantly, This paper proposes environment design in the life simulation game The Sims as a novel platform and challenge for testing there are no “win” conditions beyond any personal goals that divergent search algorithms. In this domain, which includes the player might set. However, a Sim can die if certain needs a minimal viability criterion, the goal is to furnish a house go unmet for too long. Thus, The Sims contains two separate with objects that satisfy the physical needs of a simulated yet related gameplay challenges: 1) designing a house with agent. Importantly, the large number of objects available to sufficient objects to prevent a Sim’s needs from becoming the player (whether human or automated) affords a wide vari- critically low, and 2) selecting interactions with these ob- ety of solutions to the underlying design problem. Empirical jects such that the Sim’s decaying need satisfaction levels studies in a novel open source simulator called SimSim in- are sufficiently replenished. vestigate the ability of novelty-based evolutionary algorithms This paper introduces a new open source simulator based to effectively generate viable environment designs. on house design in The Sims and demonstrates its util- ity for exploring minimal-criterion-based search algorithms. Introduction Though prior work by e.g. Yu et al. (2011) has explored opti- Sandbox games require constrained creativity and exploring mizing furniture arrangement for realistic living spaces, the complex design spaces while avoiding nonviable portions. goal herein is to discover quality diversity in the context of Examples of such games include Minecraft1, in which play- minimal viability criteria. The new experimental domain and ers must build shelters out of natural resources, and SimC- accompanying source code are the primary contributions of ity2, which requires players to set zoning policies and oth- this paper, in addition to arguing more generally that sand- erwise allocate resources to enable simulated city popula- box games are interesting testbeds for AI and, more specifi- tion growth. This genre is unique because success can be cally, for divergent search algorithms. First, existing work on achieved using an enormous variety of strategies. While this this class of algorithms is reviewed. The SimSim Sims simu- relative freedom creates a unique gaming experience for hu- lator is then introduced and key experiment-enabling differ- man players, it also presents an underexplored challenge for ences from the original game are noted. Experiments with game AI: finding a diversity of creative artifacts that satisfy a variants of an algorithm called minimal criterion novelty set of minimal constraints. Such domains can serve as an apt search are then reported, highlighting the novel platform’s testbed for an underexplored class of divergent evolutionary utility for future studies on divergent evolutionary search al- search algorithms that are driven primarily by binary mini- gorithms and procedural content generation. mal fitness criteria instead of continuous fitness gradients. In The Sims ((c) 2008 Electronic Arts, Inc.), the player controls simulated humans called Sims that inhabit a house, which can be either preconstructed or built and furnished by the player. An example human-built house is shown in Fig- ure 1. Each Sim has a set of needs (Hunger, Energy, Social, Fun, Bladder, and Hygiene) that must be satisfied, with sat- isfaction levels decreasing over time. Interactions with ob- jects, such as furniture, and with other Sims can have posi- tive and negative effects on need satisfaction levels. The pri- Figure 1: A human-designed house in The Sims 3. The Sim- ∗Sul sul means hello in Simlish, the fictional language spoken Sim simulator simplifies the design problem into that of de- in The Sims. signing a single functional room (with each furniture object Copyright c 2020, Association for the Advancement of Artificial occuping a single tile), though the simulator also supports Intelligence (www.aaai.org). All rights reserved. evolving entire houses. 1Copyright (c) 2011 Mojang 2Copyright (c) 1989 Electronic Arts, Inc 182 Background in SimCity. Minecraft has become an increasingly popular Evolutionary computation (EC) is a class of biologically- domain for creative AI research in games. In the Genera- inspired algorithms encoding points in a search space as tive Design in Minecraft (GDMC) competition (Salge et al. genomes (i.e. mutatable parameter vectors). While EC meth- 2020), the primary challenge is for an algorithm to generate ods differ in terms of genome representation (and potentially plausible settlements on arbitrary terrains. Submissions are other features), they all share a common approach of explor- scored by human judges with respect to adaptation, function- ing a search space by mutating and sometimes combining ality, evocative narrative, and aesthetics. Importantly, while genomes representing already-discovered individuals. The the theme of generative design is shared between GDMC general idea, motivated by the concept of “survival of the and SimSim, and while GDMC implicitly rewards algo- fittest”, is to iteratively create a population of genomes, rithms that can generate a diversity of high-quality designs score them on some fitness-bearing domain, and then select (by evaluating submitted algorithms on multiple terrains), the elites as parents for the next generation. GDMC does not incorporate the survival aspect of Minecraft ? (?) turned this idea on its head, demonstrating that and thus is not an ideal testbed for minimal-criterion-based 3 novelty alone, without fitness-based selection, could solve algorithms. Inversely, the MineRL competition focuses on a maze navigation problem not solvable by fitness-based high-performing agent gameplay behaviors, with little em- search alone. This idea was later extended by minimal cri- phasis on creativity. ? (?) introduced a simplified Minecraft teria novelty search (MCNS) (?), which increases search domain called Voxelbuild explicitly designed for evaluating efficiency by requiring that individuals satisfy some mini- divergent evolutionary search algorithms. However, this sys- mal fitness threshold in order to reproduce. This algorithm tem removes the Minecraft survival mechanic, thereby re- was transformative because it emphasized “survival of the moving the minimal viability criterion from the game. fittest” instead of “survival of the fit enough”, allowing more room for evolutionary creativity. Further innovations SimSim: A Sims Simulator on MCNS include progressive minimal criterion novelty The SimSim simulation4 replicates select game mechanics search (Gomes, Urbano, and Christensen 2012), evolution from The Sims for the purpose of exploring algorithms that in Chromaria (?), minimal criterion coevolution (Brant and find a diversity of high-quality designs with minimal viabil- Stanley 2017), and POET (Wang et al. 2019). ity criteria. This section first describes game mechanics in Novelty search, in part inspired a recently popular class The Sims before noting how SimSim is different. of evolutionary algorithms called quality diversity (QD) In The Sims, a human player commands one or more algorithms that aim to discover a wide variety of high- Sim agents to navigate to particular objects and interact with performing genomes within a particular search space. Most them, replenishing one or more needs. Sims can also select QD algorithms derive from either novelty search with local their own goals probabilistically; when not controlled by the competition (NSLC)(?) or MAP-Elites (?). QD algorithms player, Sims use A* pathfinding to move towards one of the have also been used for procedurally generated game con- four highest-ranked objects according to impact on “happi- tent such as spaceships (Liapis et al. 2013), strategy game ness” (combined Sim needs ordered by Maslow’s Hierarchy maps (Liapis, Yannakakis, and Togelius 2015), bullet hell of Needs). This decision-making process can also be influ- game levels (Khalifa et al. 2018), and RPG dungeons (Al- enced by distance to objects, personality motives, interaction varez et al. 2019). Gravina et al. (2019) give a review of with other Sims, and mood (Bourse 2012). QD+PCG work. In SimSim, a single agent (Algorithm 1) lives in one-room Though intentionally divergent, QD algorithms are fre- houses and interacts with furniture objects. The simulator quently cast as optimizers. More critically, the domains used contains 70 hand-coded objects, each with a vector of effects to evaluate them rarely capture the prolific creative poten- on Sim needs. For the experiments in this paper, objects are tial of evolutionary generative systems. Initial experiments placed by evolutionary algorithms, but other strategies could focused on single-objective maze navigation tasks. Subse- be implemented. There is no player interaction, and the Sim quent maze domains involved more