Anticipatory Thinking Challenges in Open Worlds: Risk Management

Anticipatory thinking drives our ability to manage risk - identification and mitigation - in everyday life, from bringing an umbrella when it might rain to buying car insurance. As AI systems become part of everyday life, they too have begun to manage risk. Autonomous vehicles log millions of miles, StarCraft and Go agents have similar capabilities to humans, implicitly managing risks presented by their opponents. To further increase performance in these tasks, out-of-distribution evaluation can characterize a model's bias, what we view as a type of risk management. However, learning to identify and mitigate low-frequency, high-impact risks is at odds with the observational bias required to train machine learning models. StarCraft and Go are closed-world domains whose risks are known and mitigations well documented, ideal for learning through repetition. Adversarial filtering datasets provide difficult examples but are laborious to curate and static, both barriers to real-world risk management. Adversarial robustness focuses on model poisoning under the assumption there is an adversary with malicious intent, without considering naturally occurring adversarial examples. These methods are all important steps towards improving risk management but do so without considering open-worlds. We unify these open-world risk management challenges with two contributions. The first is our perception challenges, designed for agents with imperfect perceptions of their environment whose consequences have a high impact. Our second contribution are cognition challenges, designed for agents that must dynamically adjust their risk exposure as they identify new risks and learn new mitigations. Our goal with these challenges is to spur research into solutions that assess and improve the anticipatory thinking required by AI agents to manage risk in open-worlds and ultimately the real-world.Comment: 4 pages, 3 figures, appeared in the non-archival AAAI 2022 Spring Syposium on "Designing Artificial Intelligence for Open Worlds

The NetHack learning environment

Progress in Reinforcement Learning (RL) algorithms goes hand-in-hand with the development of challenging environments that test the limits of current methods. While existing RL environments are either sufficiently complex or based on fast simulation, they are rarely both. Here, we present the NetHack Learning Environment (NLE), a scalable, procedurally generated, stochastic, rich, and challenging environment for RL research based on the popular single-player terminal-based roguelike game, NetHack. We argue that NetHack is sufficiently complex to drive long-term research on problems such as exploration, planning, skill acquisition, and language-conditioned RL, while dramatically reducing the computational resources required to gather a large amount of experience. We compare NLE and its task suite to existing alternatives, and discuss why it is an ideal medium for testing the robustness and systematic generalization of RL agents. We demonstrate empirical success for early stages of the game using a distributed Deep RL baseline and Random Network Distillation exploration, alongside qualitative analysis of various agents trained in the environment. NLE is open source and available at https://github.com/facebookresearch/nle

Learning to Speak and Act in a Fantasy Text Adventure Game

We introduce a large scale crowdsourced text adventure game as a research platform for studying grounded dialogue. In it, agents can perceive, emote, and act whilst conducting dialogue with other agents. Models and humans can both act as characters within the game. We describe the results of training state-of-the-art generative and retrieval models in this setting. We show that in addition to using past dialogue, these models are able to effectively use the state of the underlying world to condition their predictions. In particular, we show that grounding on the details of the local environment, including location descriptions, and the objects (and their affordances) and characters (and their previous actions) present within it allows better predictions of agent behavior and dialogue. We analyze the ingredients necessary for successful grounding in this setting, and how each of these factors relate to agents that can talk and act successfully

Scalable automated machine learning

Undergraduate thesis submitted to the Department of Computer Science, Ashesi University, in partial fulfillment of Bachelor of Science degree in Computer Science, May 2020Automated machine learning holds a lot of promise to revolutionize and democratize the field of artificial intelligence. Neural architecture search is one of the main components of AutoML and is usually very computationally expensive. Autokeras is a framework that proposes a Bayesian optimization approach to neural architecture search in order to make it more efficient [8]. AutoKeras suffers from two major limitations: (i) the lack of support for parallel Bayesian optimization, which limits applicability in distributed settings and (ii) a slow start issue which limits the performance when time is limited. Solving these two problems would make Autokeras more flexible, and allow it to scale to the available resources of the user. We address these two problems. First we design and implement two algorithms for parallel bayesian optimization. Then we incorporate a greedy algorithm to tackle the slow start problem. To evaluate the performance of those algorithms, we first evaluate the Autokeras Bayesian searcher and compare the results to the algorithms we have implemented. On a Tesla T4 GPU, running for 12 hours, the Bayesian searcher got to 80.9% for. Our first parallel algorithm, GP-UCB-PE got 81.85% on 4 GPUs for 12 hours. Our second parallel algorithm, GP-BUCB got 81.89% on GPUs for 12 hours. By incorporating the greedy approach, we achieved 86.78% after running for 3 hours.Ashesi Universit

The 2011 International Planning Competition

After a 3 years gap, the 2011 edition of the IPC involved a total of 55 planners, some of them versions of the same planner, distributed among four tracks: the sequential satisficing track (27 planners submitted out of 38 registered), the sequential multicore track (8 planners submitted out of 12 registered), the sequential optimal track (12 planners submitted out of 24 registered) and the temporal satisficing track (8 planners submitted out of 14 registered). Three more tracks were open to participation: temporal optimal, preferences satisficing and preferences optimal. Unfortunately the number of submitted planners did not allow these tracks to be finally included in the competition. A total of 55 people were participating, grouped in 31 teams. Participants came from Australia, Canada, China, France, Germany, India, Israel, Italy, Spain, UK and USA. For the sequential tracks 14 domains, with 20 problems each, were selected, while the temporal one had 12 domains, also with 20 problems each. Both new and past domains were included. As in previous competitions, domains and problems were unknown for participants and all the experimentation was carried out by the organizers. To run the competition a cluster of eleven 64-bits computers (Intel XEON 2.93 Ghz Quad core processor) using Linux was set up. Up to 1800 seconds, 6 GB of RAM memory and 750 GB of hard disk were available for each planner to solve a problem. This resulted in 7540 computing hours (about 315 days), plus a high number of hours devoted to preliminary experimentation with new domains, reruns and bugs fixing. The detailed results of the competition, the software used for automating most tasks, the source code of all the participating planners and the description of domains and problems can be found at the competition’s web page: http://www.plg.inf.uc3m.es/ipc2011-deterministicThis booklet summarizes the participants on the Deterministic Track of the International Planning Competition (IPC) 2011. Papers describing all the participating planners are included

Metalinear cinematic narrative : theory, process, and tool

Thesis (Ph.D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 1999.Includes bibliographical references (leaves 207-218).Media entertainment technology is evolving rapidly. From radio to broadcast television to cable television, from motion picture film to the promise of digital video disks, as the media evolves, so do the stories told over these media. We already share many more stories and more types of stories from many more sources than we did a decade ago. This is due in part to the development of computer technology, the globalization of computer networks, and the emerging new medium which is an amalgam of television and the internet. The storyteller will need to invent new creative processes and work with new tools which support this new medium, this new narrative form. This thesis proposes the name Metalinear Narrative for the new narrative form. The metalinear narrative is a collection of small related story pieces designed to be arranged in many different ways, to tell many different linear stories from different points of view, with the aid of a story engine. Agent Stories is the software tool developed as part of this research for designing and presenting metalinear cinematic narratives. Agent Stories is comprised of a set of environments for authoring pieces of stories, authoring the relationships between the many story pieces, and for designing an abstract narrative structure for sequencing those pieces. Agent Stories also provides a set of software agents called story agents, which act as the drivers of the story engine. My thesis is that a writing tool which offers the author knowledgeable feedback about narrative construction and context during the creative process is essential to the task of creating metalinear narratives of significant dimension.by Kevin Michael Brooks.Ph.D

Kaleidoscope : fictional genres and probable worlds

If fictional narratives do indeed create alternate possible worlds, and these alternate possible worlds are both enacted by and embody generic differences, as possible worlds narratology suggests, what happens when the genre of a novel changes as the text unfolds? Does a change of genre equate to a change of fictional narrative world, or a change within the fictional narrative world? If worldlikeness is recognised as a prerequisite for immersion, do genre shifts necessarily entail a disruption of immersion, and is such a potential disruption temporary or lasting? From a creative practice perspective, how and why would a writer steer their novel from one generic orientation to another? And from a possible-worlds theoretical perspective, what does the analysis of such genre changes reveal about the process of identifying genre, the role of genre in the creation of fictional narrative worlds, and the effectiveness of the concept of possibility in accounting for generic differences? This project investigates these questions through creative experimentation and critical examination with the aim of uncovering new insights into the fundamental nature of both genre and fictional narrative worlds. The novel Kaleidoscope attempts to unravel the strategies involved in implementing changes of genre within texts, testing the relationship between genre and immersion within a many-worlds ontological structure and finding significant gaps in existing understandings of what genre is and does. Informed by the findings of this creative process, the critical exegesis applies a possible-worlds informed analysis of genre to the genre-shifting works of César Aira, uncovering not only a greater understanding of the functions and functioning of genre but also important limitations in current narratological approaches to generic analysis. By attempting to apply Marie-Laure Ryan’s seminal semantic typology of fiction to the analysis of Aira’s genre-shifting works, possibility alone is found to provide an insufficient basis for generic differentiation, while the concept of probability – largely overlooked within contemporary narratology – emerges as a vital conceptual tool. The identification of probability emphasis, the generically probable and improbable, and probable accessibility relations in the analysis of genre-shifting texts reveals the importance of probability, not only to analysis, but in the development of fictional worlds. Through the interaction of creative practice and critical examination, these worlds are found to depend as much on the probable as the possible, complicating current conceptualisations of fiction in terms of possible worlds and suggesting that much remains to be discovered about the role and relevance of genre, the relationship between worldlikeness and immersion, and the probability, fictionality, and worldness of fictional narrative worlds

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Games and Time

Video games are a medium uniquely immersed in time. While the topic of time and games has been broached by many in the field of game studies, its centrality to both how games function and the experience of playing games remains underexamined. Reading games as literary texts, this holistic study uses queer and social theories to survey the myriad of ways games play with time. I argue games are time machines, each idiosyncratically allows players to experience time differently from traditional linear time. Beyond games with literal time machines, this dissertation examines games which structure themselves around labyrinthine and existential loops. It also considers real-time, or games competitively organized around time and those which change over time, in a sense, aging. Regardless of the subject, this dissertation seeks to illuminate the complexities of games and time, and argues that, despite their many conflicting messages about the topic, they all have something meaningful to say about the human experience of time