Doing Interface Ecology: the Practice of Metadisciplinarity

Total Page:16

File Type:pdf, Size:1020Kb

Doing Interface Ecology: the Practice of Metadisciplinarity Doing Interface Ecology: The Practice of Metadisciplinarity Andruid Kerne Interface Ecology Lab | Center for Study of Digital Libraries Computer Science Dept., Texas A&M University Abstract What are the systems of representation that are The interface can be modeled as an ecosystem: con- brought into relationships by interfaces? Let's start with nected, dynamic, and characterized by relationships. a simple example: a typical personal computer, con- This model is predicated on a process of working with nected to the Internet. Immediately, we have the "sen- the interface as a border zone between heterogeneous sation systems" of a human being seeing a computer systems of representation. This paper uses sensation, screen, touching a mouse and a keyboard. The physi- embodiment, and semiotics to initiate this work cal is translated into the electronic, and vice versa. process by addressing the range of systems of repre- The analog representations from the physical, real sentation that are involved in its own production. This world are converted to and from the digital representa- "presence of the theorist" creates a self-referential tions processed by the computer. We have layers of metastructure. hardware and software. By referring to a "typical per- sonal computer," I meant to suggest Intel processors As an alternative to the beneficial but ad-hoc assem- and a Microsoft Operating System. Thus, we have the blages of multi-, inter-, and transdisciplinary approach- old anti-trust litigation between Apple and Microsoft, es, the ecosystems approach proposes that the mesh- and more current jostlings with Linux and those who ing of systems of representation is an inherent proper- believe in free software. Somewhere in this scenario, ty of interface phenomena. The meshing process the voices of the hardware and software engineers are causes elements from the involved representational echoing. Perhaps they worked very long hours; per- systems to recombine and form hybrids. Recombinant haps they were content with their rewards; perhaps information is a structural formula for creating new they felt exploited. knowledge, which can be intentionally invoked for that purpose. The interface of the personal computer puts the user in touch The theorist is part of the environment that s/he theo- rizes about. The products of theorizing are information with the assemblers, in a tactile artifacts that are also part of the environment; they and economical way. The eco- themselves function as interfaces. The term metadisci- plinary is developed here in order to describe the nomic relationships leave a sticky inherent and self-referential nature of this structure. trail of money that gets under my The structure of metadisciplinarity connects theory and fingernails. As I close the case practice, which stands in direct contrast with other studies approaches such as performance studies, and get back to writing this paper, which separates itself from theater practice. I worry it might gum up the keyboard. Border zones where systems of representation meet An interface is a border zone where systems of Those working on the "low level" manufacturing were representation come into contact. It is a mem- probably women. [2] Among these, the ones who built brane, regulating the exchange of vital messages the primary circuit board, known as the motherboard, from one side to the other. The more open the were probably Chinese. [3] Let's open the machine's membrane, the more flow, the more new combina- case and check it out. Perusing the motherboard, inte- tions that an interface supports. Particular mem- grated circuit labels indicate that they were made in brane structures can act as filters, tuning feedback countries such as China (again), Malaysia, the loops. Philippines, El Salvador, and Guatemala. According to -- Interface Ecosystem, The Fundamental Unit of the LABORSTA database of the International Labour Information Age Ecology [1] Organization, the average female electronic equipment assembler in China earned $106 per month during Intelligent Agent 6.1.1 interface.kerne.01 2000. [4] The interface of the personal computer puts the user in touch with the assemblers, in a tactile and We have yet to begin browsing, to economical way. The economic relationships leave a encounter "content." Yet, already, sticky trail of money that gets under my fingernails. As I feel like I am caught in the sticky I close the case and get back to writing this paper, I worry it might gum up the keyboard. web of a horror movie or, at least, a haunted house. I am typing this So many relationships already, and we have not yet gotten to the network. There are some open standards paper in Microsoft Word, wanting here, the TCP/IP protocol stack, and higher level pro- to experience a blank slate for tocols such as HTTP for the Web, SMTP and POP for writing but yearnings for a tabula e-mail. There are standards, too, for declarative lan- guages such as Hypertext Markup Language (HTML) rasa give way to the multilayered and Cascading Style Sheets (CSS). Of course, no web reality of a highly structured, browser is complete without the potential for search. So Google is here, too. complex ecosystem of entities and representations. There are At the time of this writing, it seems that there is an too many windows, too many 85% likelihood that the web browser used by people accessing the Web is Microsoft Internet Explorer. [5] menus, too many entries, too So here we have another of Microsoft's anti-trust litiga- many buttons. tion scenarios, this one involving Netscape. The U.S. Department of Justice and the Sherman Antitrust law In my web browser, I jump from a background article have a presence. So have various states and judges, in an industry rag [9] to the <<conference or journal and the European Union. Operating systems, applica- x>> web site to the New York Times for today's news. I tion systems, legal systems, economic systems, drop in on Slashdot, then move on to the ACM Digital national systems, international systems, and multina- Library for more research, then to whatisthematrix.com tional systems are interconnected in complex flows. to find a link to a video, which I connect to an entry about Baudrillard's concept of hyperreality in the site Writing this, I am sitting in front of an IBM laptop, so for the metadisciplinary undergraduate class -- product designers are represented, too. Selker's work "Structures of Interactive Information" -- that I am on the Trackpoint sits in front of me. [6] So does the teaching. [10] The hyperreal refers to the replacement brand identity of Big Blue. Paul Rand's logo is present of real world representations, relationships, and values and accounted for. In my memory, a legion of ad cam- by electronic ones. [11] I keep myself over-stimulated paigns reverberates, from "Solutions for A Small to the point of anxiety, I am saturated with information, Planet," to "His name is Linux… The future is open." out of habit. The sound over sound sounds muddy. This little computer is a meeting point for many codes. Vast infrastructures of research and production are at Codes of signification. Codes of automata: operators work; universities, national labs, and corporations and operands. Codes of expression. Codes of control. carve out highly connected roles. [7] They constitute A mesh of media renderings, disciplinary structures of the social relations of digital production; they selective- methodology, cultural groundings, and epistemological ly filter what gets produced, and what gets omitted. foundations is formed. As I consider all these systems of representation that are in play, all of this significa- We have yet to begin browsing, to encounter "con- tion, I return to the start of this exegesis, to the role of tent." Yet, already, I feel like I am caught in the sticky my body-mind. I return to sensation, the core of user web of a horror movie or, at least, a haunted house. I experience. I turn to phenomenology (Merleau-Ponty): am typing this paper in Microsoft Word, wanting to "The sensor and the sensible do not stand in relation experience a blank slate for writing but yearnings for a to each other as two mutually external terms… It is my tabula rasa give way to the multilayered reality of a gaze which subtends colour … In this transaction highly structured, complex ecosystem of entities and between the subject of sensation and the sensible it representations. There are too many windows, too cannot be held that one acts while the other suffers many menus, too many entries, too many buttons. The the action, or that one confers significance on the magical number that defines the capacity of working other." [12] memory, 7±2, is violated repeatedly. [8] Branded tool- bars pervade the digital parchment. I <alt-tab> back According to Merleau-Ponty, sensation is an active and forth from the word processor to web browsers, process. Sensation situates the individual in her envi- scanning for supporting materials, and then writing ronment. In the field of artificial intelligence and robot- some more. Icons trail my every move. ics, Brooks' model of embodiment operates similarly: Intelligent Agent 6.1.1 interface.kerne.02 perception and action are integrated through cognition. ratory, "a site … through which artists, scientists, tech- [13] Through the process of sensation, through per- nologists, and theorists commingle" [24] and mentions ception and action connected, at the nexus of the ZKM, Banff, Ars Electronica, and IRCAM as examples. interface, I encounter all of these systems of represen- Gibbons [25], Norman [26], Century [27], and Ascott tation, their forms as media, the rules that govern their [28] call these assemblages transdisciplinary -- trans- production and the subversions that attempt to coun- disciplinary research is said to "interpenetrate discipli- tervail the underlying epistemologies, and their nary epistemologies" [29]; it is "transgressive." [30] methodologies of practice.
Recommended publications
  • Intelligent Agents
    Intelligent Agents Authors: Dr. Gerhard Weiss, SCCH GmbH, Austria Dr. Lars Braubach, University of Hamburg, Germany Dr. Paolo Giorgini, University of Trento, Italy Outline Abstract ..................................................................................................................2 Key Words ..............................................................................................................2 1 Foundations of Intelligent Agents.......................................................................... 3 2 The Intelligent Agents Perspective on Engineering .............................................. 5 2.1 Key Attributes of Agent-Oriented Engineering .................................................. 5 2.2 Summary and Challenges................................................................................ 8 3 Architectures for Intelligent Agents ...................................................................... 9 3.1 Internal Agent Architectures .......................................................................... 10 3.2 Social Agent Architectures............................................................................. 11 3.3 Summary & Challenges ................................................................................. 12 4 Development Methodologies................................................................................ 13 4.1 Overall Characterization ................................................................................ 13 4.2 Selected AO Methodologies..........................................................................
    [Show full text]
  • Intelligent Agents - Catholijn M
    ARTIFICIAL INTELLIGENCE – Intelligent Agents - Catholijn M. Jonker and Jan Treur INTELLIGENT AGENTS Catholijn M. Jonker and Jan Treur Vrije Universiteit Amsterdam, Department of Artificial Intelligence, Amsterdam, The Netherlands Keywords: Intelligent agent, Website, Electronic Commerce Contents 1. Introduction 2. Agent Notions 2.1 Weak Notion of Agent 2.2 Other Notions of Agent 3. Primitive Agent Concepts 3.1 External primitive concepts 3.2 Internal primitive concepts 3.3. An Example Analysis 4. Business Websites 5. A Generic Multi-Agent Architecture for Intelligent Websites 6. Requirements for the Website Agents 6.1 Characteristics and Requirements for the Website Agents 6.2 Characteristics and Requirements for the Personal Assistants 7. The Internal Design of the Information Broker Agents 7.1 A Generic Information Broker Agent Architecture 7.2 The Website Agent: Internal Design 7.3 The Personal Assistant: Internal Design Glossary Bibliography Biographical Sketches Summary In this article first an introduction to the basic concepts of intelligent agents is presented. The notion of weak agent is briefly described, and a number of external and internal primitive agent concepts are introduced. Next, as an illustration, application of intelligent agents to business Websites is addressed. An agent-based architecture for intelligentUNESCO Websites is introduced. Requirements – EOLSS of the agents that play a role in this architecture are discussed. Moreover, their internal design is discussed. 1. IntroductionSAMPLE CHAPTERS The term agent has become popular, and has been used for a wide variety of applications, ranging from simple batch jobs and simple email filters, to mobile applications, to intelligent assistants, and to large, open, complex, mission critical systems (such as systems for air traffic control).
    [Show full text]
  • Multi-Agent Reinforcement Learning: an Overview∗
    Delft University of Technology Delft Center for Systems and Control Technical report 10-003 Multi-agent reinforcement learning: An overview∗ L. Bus¸oniu, R. Babuska,ˇ and B. De Schutter If you want to cite this report, please use the following reference instead: L. Bus¸oniu, R. Babuska,ˇ and B. De Schutter, “Multi-agent reinforcement learning: An overview,” Chapter 7 in Innovations in Multi-Agent Systems and Applications – 1 (D. Srinivasan and L.C. Jain, eds.), vol. 310 of Studies in Computational Intelligence, Berlin, Germany: Springer, pp. 183–221, 2010. Delft Center for Systems and Control Delft University of Technology Mekelweg 2, 2628 CD Delft The Netherlands phone: +31-15-278.24.73 (secretary) URL: https://www.dcsc.tudelft.nl ∗This report can also be downloaded via https://pub.deschutter.info/abs/10_003.html Multi-Agent Reinforcement Learning: An Overview Lucian Bus¸oniu1, Robert Babuskaˇ 2, and Bart De Schutter3 Abstract Multi-agent systems can be used to address problems in a variety of do- mains, including robotics, distributed control, telecommunications, and economics. The complexity of many tasks arising in these domains makes them difficult to solve with preprogrammed agent behaviors. The agents must instead discover a solution on their own, using learning. A significant part of the research on multi-agent learn- ing concerns reinforcement learning techniques. This chapter reviews a representa- tive selection of Multi-Agent Reinforcement Learning (MARL) algorithms for fully cooperative, fully competitive, and more general (neither cooperative nor competi- tive) tasks. The benefits and challenges of MARL are described. A central challenge in the field is the formal statement of a multi-agent learning goal; this chapter re- views the learning goals proposed in the literature.
    [Show full text]
  • An Exploration in Stigmergy-Based Navigation Algorithms
    From Ants to Service Robots: an Exploration in Stigmergy-Based Navigation Algorithms عمر بهر تيری محبت ميری خدمت گر رہی ميں تری خدمت کےقابل جب هوا توچل بسی )اقبال( To my late parents with love and eternal appreciation, whom I lost during my PhD studies Örebro Studies in Technology 79 ALI ABDUL KHALIQ From Ants to Service Robots: an Exploration in Stigmergy-Based Navigation Algorithms © Ali Abdul Khaliq, 2018 Title: From Ants to Service Robots: an Exploration in Stigmergy-Based Navigation Algorithms Publisher: Örebro University 2018 www.publications.oru.se Print: Örebro University, Repro 05/2018 ISSN 1650-8580 ISBN 978-91-7529-253-3 Abstract Ali Abdul Khaliq (2018): From Ants to Service Robots: an Exploration in Stigmergy-Based Navigation Algorithms. Örebro Studies in Technology 79. Navigation is a core functionality of mobile robots. To navigate autonomously, a mobile robot typically relies on internal maps, self-localization, and path plan- ning. Reliable navigation usually comes at the cost of expensive sensors and often requires significant computational overhead. Many insects in nature perform robust, close-to-optimal goal directed naviga- tion without having the luxury of sophisticated sensors, powerful computational resources, or even an internally stored map. They do so by exploiting a simple but powerful principle called stigmergy: they use their environment as an external memory to store, read and share information. In this thesis, we explore the use of stigmergy as an alternative route to realize autonomous navigation in practical robotic systems. In our approach, we realize a stigmergic medium using RFID (Radio Frequency Identification) technology by embedding a grid of read-write RFID tags in the floor.
    [Show full text]
  • Expert Assessment of Stigmergy: a Report for the Department of National Defence
    Expert Assessment of Stigmergy: A Report for the Department of National Defence Contract No. W7714-040899/003/SV File No. 011 sv.W7714-040899 Client Reference No.: W7714-4-0899 Requisition No. W7714-040899 Contact Info. Tony White Associate Professor School of Computer Science Room 5302 Herzberg Building Carleton University 1125 Colonel By Drive Ottawa, Ontario K1S 5B6 (Office) 613-520-2600 x2208 (Cell) 613-612-2708 [email protected] http://www.scs.carleton.ca/~arpwhite Expert Assessment of Stigmergy Abstract This report describes the current state of research in the area known as Swarm Intelligence. Swarm Intelligence relies upon stigmergic principles in order to solve complex problems using only simple agents. Swarm Intelligence has been receiving increasing attention over the last 10 years as a result of the acknowledgement of the success of social insect systems in solving complex problems without the need for central control or global information. In swarm- based problem solving, a solution emerges as a result of the collective action of the members of the swarm, often using principles of communication known as stigmergy. The individual behaviours of swarm members do not indicate the nature of the emergent collective behaviour and the solution process is generally very robust to the loss of individual swarm members. This report describes the general principles for swarm-based problem solving, the way in which stigmergy is employed, and presents a number of high level algorithms that have proven utility in solving hard optimization and control problems. Useful tools for the modelling and investigation of swarm-based systems are then briefly described.
    [Show full text]
  • Intelligent Agent
    INTELLIGENT AGENT INTELLIGENT AGENT By, P.V. Raja Shekar & CH. Madhuri MRITS MCA FINAL YEAR. INTRODUCTION 1 | P a g e INTELLIGENT AGENT 1 What is artificial intelligence? 2 Branches of artificial intelligence. 3 Introduction to intelligence agent. 4 What is an agent. 5 What is a rational agent. 6 Common attributes of intelligent agent 7 What is bounded rationality. 8 What is an environment and different types of them. 9 Different agent architectures. 10 Applications . 11 Limitations . 12 Conclusion . Artificial Intelligence(AI) Artificial intelligence (AI) is both the intelligence of machines and the branch of computer science which aims to create it. Artificial Intelligence is a combination of cognitive science, linguistics, ontology, physiology, psychology, philosophy, operations research, economics, control theory, neuroscience, computer science, probability, optimization and logic. AI is a very 2 | P a g e INTELLIGENT AGENT large subject-matter. It consists of many different fields, from machine vision to expert systems. The aim of all the fields is the creation of machines that can "think". Researches hope that AI machines will be capable of reasoning, knowledge, learning, communication, planning, perception and the ability to move and manipulate objects. What's it all about? Artificial Intelligence (AI) is the science and engineering of creating intelligent machines and computer programs. It is related to similar tasks of using computers to understand human intelligence but AI does not only have to confine itself to methods that are biologically observable. The interest in AI has commonly increased since years and some classical sub- disciplines like robotics, language processing, or natural computing have produced reliable solutions Intelligence is prediction Intelligence is the ability to predict what comes next, even when faced with incomplete knowledge.
    [Show full text]
  • A Systematic Approach to Artificial Agents
    A Systematic Approach to Artificial Agents Mark Burgin1 and Gordana Dodig-Crnkovic2 1 Department of Mathematics, University of California, Los Angeles, California, USA, 2 School of Innovation, Design and Engineering, Mälardalen University, Sweden, Abstract. Agents and agent systems are becoming more and more important in the development of a variety of fields such as ubiquitous computing, ambient intelligence, autonomous computing, intelligent systems and intelligent robotics. The need for improvement of our basic knowledge on agents is very essential. We take a systematic approach and present extended classification of artificial agents which can be useful for understanding of what artificial agents are and what they can be in the future. The aim of this classification is to give us insights in what kind of agents can be created and what type of problems demand a specific kind of agents for their solution. Keywords: Artificial agents, classification, perception, reasoning, evaluation, mobility 1 Introduction Agents are advanced tools people use to achieve different goals and to various problems. The main difference between ordinary tools and agents is that agents can function more or less independently from those who delegated agency to the agents. For a long time people used only other people and sometimes animals as their agents. Developments in information processing technology, computers and their networks, have made possible to build and use artificial agents. Now the most popular approach in artificial intelligence is based on agents. Intelligent agents form a basis for many kinds of advanced software systems that incorporate varying methodologies, diverse sources of domain knowledge, and a variety of data types.
    [Show full text]
  • Adversarial Attack and Defense in Reinforcement Learning-From AI Security View Tong Chen , Jiqiang Liu, Yingxiao Xiang, Wenjia Niu*, Endong Tong and Zhen Han
    Chen et al. Cybersecurity (2019) 2:11 Cybersecurity https://doi.org/10.1186/s42400-019-0027-x SURVEY Open Access Adversarial attack and defense in reinforcement learning-from AI security view Tong Chen , Jiqiang Liu, Yingxiao Xiang, Wenjia Niu*, Endong Tong and Zhen Han Abstract Reinforcement learning is a core technology for modern artificial intelligence, and it has become a workhorse for AI applications ranging from Atrai Game to Connected and Automated Vehicle System (CAV). Therefore, a reliable RL system is the foundation for the security critical applications in AI, which has attracted a concern that is more critical than ever. However, recent studies discover that the interesting attack mode adversarial attack also be effective when targeting neural network policies in the context of reinforcement learning, which has inspired innovative researches in this direction. Hence, in this paper, we give the very first attempt to conduct a comprehensive survey on adversarial attacks in reinforcement learning under AI security. Moreover, we give briefly introduction on the most representative defense technologies against existing adversarial attacks. Keywords: Reinforcement learning, Artificial intelligence, Security, Adversarial attack, Adversarial example, Defense Introduction best moves with reinforcement learning from games of Artificial intelligence (AI) is providing major break- self-play. Meanwhile, for Atari game, Mnih et al. (2013) throughs in solving the problems that have withstood presented the first deep learning model to learn con- many attempts of natural language understanding, speech trol policies directly from high-dimensional sensory input recognition, image understanding and so on. The latest using reinforcement learning. Moreover, Liang et al. (Guo studies (He et al.
    [Show full text]
  • Extending Universal Intelligence Models with Formal Notion of Representation
    Extending Universal Intelligence Models with Formal Notion of Representation Alexey Potapov, Sergey Rodionov AIDEUS, Russia {potapov,rodionov}@aideus.com Abstract. Solomonoff induction is known to be universal, but incomputable. Its approximations, namely, the Minimum Description (or Message) Length (MDL) principles, are adopted in practice in the efficient, but non-universal form. Recent attempts to bridge this gap leaded to development of the Repre- sentational MDL principle that originates from formal decomposition of the task of induction. In this paper, possible extension of the RMDL principle in the context of universal intelligence agents is considered, for which introduction of representations is shown to be an unavoidable meta-heuristic and a step toward efficient general intelligence. Hierarchical representations and model optimiza- tion with the use of information-theoretic interpretation of the adaptive reso- nance are also discussed. Key words: Universal Agents, Kolmogorov Complexity, Minimum Descrip- tion Length Principle, Representations 1 Introduction The idea of universal induction and prediction on the basis of algorithmic information theory was invented a long time ago [1]. In theory, it eliminates the fundamental problem of prior probabilities, incorrect solutions of which result in such negative practical effects as overlearning, overfitting, oversegmentation, and so on. It would be rather natural to try to develop some models of universal intelligence on this basis. However, the corresponding detailed models were published only relatively recently (e.g. [2]). Moreover, the theory of universal induction was not popular even in ma- chine learning. The reason is quite obvious – it offers incomputable methods, which additionally require training sets of large sizes in order to make good predictions.
    [Show full text]
  • Arxiv:1607.08289V4 [Cs.AI] 21 Jan 2019 Keywords: USA CA [email protected] Francisco, San FPC, Vicarious Hay J
    1 Mammalian Value Systems Gopal P. Sarma School of Medicine, Emory University, Atlanta, GA USA [email protected] Nick J. Hay Vicarious FPC, San Francisco, CA USA [email protected] Keywords: Friendly AI, value alignment, human values, biologically inspired AI, human-mimetic AI Characterizing human values is a topic deeply interwoven with the sciences, humanities, political philosophy, art, and many other human endeavors. In recent years, a number of thinkers have argued that accelerating trends in computer science, cognitive science, and related disciplines foreshadow the creation of intelligent machines which meet and ultimately surpass the cognitive abilities of human beings, thereby entangling an understanding of human values with future technological development. Contemporary research accomplishments suggest increasingly sophisticated AI systems becoming widespread and responsible for managing many aspects of the modern world, from preemptively planning users’ travel schedules and logistics, to fully autonomous vehicles, to domestic robots assisting in daily living. The extrapolation of these trends has been most forcefully described in the context of a hypothetical “intelligence explosion,” in which the capabilities of an intelligent software agent would rapidly increase due to the presence of feedback loops unavailable to biological organisms. The possibility of super- intelligent agents, or simply the widespread deployment of sophisticated, autonomous AI systems, highlights an important theoretical problem: the need to separate
    [Show full text]
  • Intelligent Agents Web-Mining Agents
    Intelligent Agents Web-Mining Agents Prof. Dr. Ralf Möller Dr. Tanya Braun Universität zu Lübeck Institut für Informationssysteme Acknowledgements • Some slides have been designed by PD Dr. Özgür Özcep. Since Özgür works in my group, those slides are not explicitly marked. Thank you nevertheless, Özgür. • Other slides have been take from lecture material provided by researchers on the web. I hope this material is indicated appropriately. Thank you all. 2 Artificial Intelligence and Intelligent Agents • Artificial intelligence (AI) is the science of systematic synthesis and analysis of computational agents that act intelligently – Agents are central to AI (and vice versa) – Intelligent agent = computational agent that acts intelligently – Talking about AI w/o talking about agents misses the point (and vice versa) • Need to technically define the notion of “acting intelligently” • AI = Science of Intelligent Systems – Systems are called computational agents in AI, or agents for short 3 Literature http://aima.cs.berkeley.edu (AIMA, 1st edition 1995) http://artint.info st (AIFCA, 1 edition 2010) 4 Literature 5 Literature 6 What is an Agent? • Anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators [AIMA-Def] Sensors Percepts ? Environment Agent Actions – Human agent Actuators eyes, ears, and other organs for sensors; hands, legs, mouth, and other body parts for actuators – Robotic agent cameras and infrared range finders for sensors; various motors for actuators – Software agent interfaces, data integration, interpretation, data manipulation/output 7 Abstractions: Agents and Environments Sensors Percepts ? Environment Agent Actions Actuators • The agent function maps from percept histories to actions: [f: P* à A] • The agent program runs on Really insist on functional a physical architecture to produce f behavior? • Agent = architecture + program 8 Reactive vs.
    [Show full text]
  • Using Stories to Teach Human Values to Artificial Agents
    The Workshops of the Thirtieth AAAI Conference on Artificial Intelligence AI, Ethics, and Society: Technical Report WS-16-02 Using Stories to Teach Human Values to Artificial Agents Mark O. Riedl and Brent Harrison School of Interactive Computing, Georgia Institute of Technology Atlanta, Georgia, USA friedl, [email protected] Abstract achieve a goal without doing anything “bad.” Value align- ment is a property of an intelligent agent indicating that it Value alignment is a property of an intelligent agent in- can only pursue goals that are beneficial to humans (Soares dicating that it can only pursue goals that are beneficial to humans. Successful value alignment should ensure and Fallenstein 2014; Russell, Dewey, and Tegmark 2015). that an artificial general intelligence cannot intention- Value alignment concerns itself with the definition of “good” ally or unintentionally perform behaviors that adversely and “bad,” which can subjectively differ from human to hu- affect humans. This is problematic in practice since it man and from culture to culture. is difficult to exhaustively enumerated by human pro- Value alignment is not trivial to achieve. As argued by grammers. In order for successful value alignment, we Soares (2015) and effectively demonstrated by Asimov’s argue that values should be learned. In this paper, we Robot series of books, it is very hard to directly specify val- hypothesize that an artificial intelligence that can read ues. This is because there are infinitely many undesirable and understand stories can learn the values tacitly held by the culture from which the stories originate. We de- outcomes in an open world.
    [Show full text]