Proceedings of the Fourteenth Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE 2018) Postmortem: MKULTRA, An Experimental AI-Based Game Ian Douglas Horswill Northwestern University, Evanston IL, USA [email protected] Abstract Development proceeded as far as a playable demo with Games are inherently situated within the cultures of their fully implement character AI and drama management. players. Players bring a wide range of knowledge and expec- However, even the demo has proven very difficult for naïve tations to a game, and the more the game suggests connec- users to play. The problem is not that the puzzles are too tions to that culture, the stronger those expectations are hard, but that learning the limits of the system is too hard. and/or the more problematic they can be. MKULTRA is an experimental, AI-heavy game that ran afoul of those issues. A succession of tutorial mechanisms were added to the It’s interesting to hear a talk about or to see demonstrated by game: autocompletion, hinting mechanisms, active prod- the author, but frustrating for players who do not already un- ding from NPCs, and finally, pop-up menus to suggest derstand its internals in some detail. promising actions to the player. But none were sufficient to In this paper, I will give a postmortem of the game, in the allow any of the 30 or so playtesters to complete the demo rough style of industry postmortems from venues such as Ga- masutra or GDC. I will discuss the goals and design of the level without help. As a result, development of the game game, what went right, what went wrong, and what I should has been suspended pending a fundamental redesign of the have done instead. In my discussions of the game’s prob- gameplay. lems, I’ll focus on the ways in which it frustrated the players’ In this paper, I will discuss the development of the game cultural expectations, and what we can learn from them for in the style of a game industry postmortem: goals, technol- the design of future games. ogy, what went right, what went wrong, and what I should have done instead. The intent here is not to document spe- Introduction cific technical innovations, since those have been written about elsewhere. It is rather to talk about why those inno- MKULTRA is an experimental, AI-heavy game intended to vations were insufficient, and how we can apply those les- explore novel, AI-centered game mechanics (I. Horswill, sons to future games. 2014b, 2014a). Roughly speaking, it consists of a 1980s- Much of the failure of the system involves the cultural style Prolog natural language system (Pereira & Shieber, expectations players brought to the game, and the game’s 1987; Pereira & Warren, 1980; David H. D. Warren & inability to fulfill them. These failures were twofold: the Pereira, 1982), combined with a 1990s-style reactive plan- game’s characters suggested a deeper understanding of hu- ner (McDermott, 1978; Sibun, 1992), running in a man culture than they actually possessed, and the game’s RPGMaker-style tile-based world. The agents in the game similarities to existing genres misled players to expect dif- – the player, the player character, and the NPCs – can all ferent kinds of gameplay than the game was able to support. interact using generative natural language dialog, including questions, imperatives, and declaratives, within a restricted grammar of English and a lexicon of a few hundred words. Goals The game was envisioned as a mystery where the player The game’s original goal was to build a next-generation in- was cast in the role of Betsy, a detective with psionic abili- teractive narrative in the tradition of Mateas and Stern’s Fa- ties. The mystery narrative provided an impetus for natural çade (Mateas & Stern, 2005). Periodic attempts by the game language interaction – the player must talk to characters to AI research community to build full-fledged games are im- collect information – and the psionic abilities afforded a portant. And while there has been a great deal of work on number of novel gameplay mechanics, most notably belief interactive narrative since Façade (see, for example (Evans injection (see below). & Short, 2014; I. D. Horswill, Montfort, & Young, 2014; Copyright © 2018, Association for the Advancement of Artificial Intelli- gence (www.aaai.org). All rights reserved. 45 Roberts & Isbell, 2008; Robertson & Young, 2015), the only Gameplay recent attempt to build a similarly complete, AI-heavy inter- active narrative piece has been Evan’s and Short’s Versu The core gameplay is similar to classic parser-based inter- platform (Evans & Short, 2013), which did not come from active fiction (Jackson-Mead & Wheeler, 2011). By typing the research community. commands in English, the player explores the world, gath- The primary technical goal was to integrate simple but ering information, poking systems to understand how they generative natural language with true semantic parsing into work, and ultimately solving puzzles. Unlike parser-based an AI-based interactive narrative. Façade did something IF, the systems being poked are autonomous, AI-driven closer to categorization of utterances than the production of NPCs running a simple NL system and reactive planner. true logical forms (Mateas & Stern, 2004); the system could All characters, including the player character (PC), go understand that you were agreeing with a character or that about pursuing their own limited goals in the absence of you had insulted them. But if you asked a character a ques- player intervention. The player intervenes by typing sen- tion, the system literally couldn’t represent the content of tences in the UI; these “thoughts” are injected into the player the specific question. character. They can be requests, questions, assertions, re- Finally, the project had a set of game design goals; it was sponses to questions, or variations such as indirect requests. an exercise in AI-based game design (Eladhari, Sullivan, If the player character is in conversation with an NPC, the Smith, & Mccoy, 2011). The central design problem it sentences are spoken by the player character to the NPC. sought to solve was to design gameplay that was robust with Otherwise, they are taken as being addressed to the player respect to the AI system’s failure modes. Any practical character. Characters, including the PC, can refuse requests character AI system will have limited knowledge, vocabu- or lie in response to questions. The only exception is belief lary, reasoning abilities, and so on. The project sought to injection, which characters cannot refuse. design the mechanics and the narrative of the game so as to The fundamental mechanics of the game are therefore compensate for those limitations (I. Horswill, 2014b). questions, requests, and belief injection. These mechanics One example of this was the belief injection mechanic. are then used to gather information and solve puzzles. The player can manipulate an NPC’s behavior by injecting false beliefs directly into its knowledge base. This is a fun Technology puzzle mechanic: how do you figure out the character’s cur- rent belief structure, so you can change it to accomplish your MKULTRA runs under the Unity3D game engine (Unity goal? It’s also a narrative alibi for the character’s inevitable Technologies, 2004). The game is free, open-source soft- failures of planning, inference, and social norms; the char- ware available on github. acter can be forgiven for doing stupid things because it’s a The AI system is written primarily in a custom-built mind-controlled zombie. Put another way, by making AI Prolog interpreter. The Prolog interpreter also implements debugging a form of gameplay, we reduce the intrusiveness Evans’ eremic logic (Evans, 2010; Evans & Short, 2014), of the AI’s fragility. The player sees it as a failure of their which provides a separate, tree-structured knowledge-base own problem solving rather than as a bug in the game itself. that has better update semantics than Prolog’s assert/retract Another example was the surfacing of the AI’s limitations interface. in the user interface. MKULTRA’s vocabulary is several The major components of the AI system consists of: hundred words, not the tens of thousands of a fluent speaker. Teaching the player what the system can and cannot under- • A reversible, semantic parser/generator based on def- stand is difficult. So the system scaffolds the player’s learn- inite-clause grammars (Pereira & Shieber, 1987; ing using a novel autocomplete mechanism (I. Horswill, Pereira & Warren, 1980). It handles single-clause 2014a). The system uses a reversible parser/generator that English sentences without quantifiers, in a variety of allows it to take a partial user inputs as they are typed, solve grammatical moods, tenses, and aspects. for its possible completions within the subset of English the • A committed-choice reactive planner inspired in part system understands, and display those completions in real by the SALIX system (Sibun, 1992). time, introducing the player to examples of sentences it un- • A dialog system that can respond speech acts involv- derstands. If there are no possible completions, the system ing questions, answers, assertions, and imperatives. knows the player has typed an invalid input and gives them It is essentially an elaborate version of CHAT-80 immediate feedback rather than letting them continue typing (David H. D. Warren & Pereira, 1982). a bad command. • A simple ternary logic programming system reason- ing about the knowledge states of characters to help them reason about the distinction between not know- ing a fact and knowing the fact to be false. 46 Demo level Although the character AI and beat sequencing systems were implemented, only a demo level was constructed be- cause of the playability problems with the game.