Posters

  PT-AI 2017    
  List of Accepted Poster Presentations  
1 Wolfhart Totschnig. Fully autonomous AI
2 Paulius Astromskis. In Critique of RoboLaw: the Model of SmartLaw
3 Selmer Bringsjord and Naveen Sundar Govindarajulu. Do Machine-Learning Machines Learn?
4 Christopher Burr, Nello Cristianini and James Ladyman. Intelligent Agents and the Manipulation of User Behaviour
5 Gordana Dodig Crnkovic. Morphologically Computing Embodied, Embedded, Enactive, Extended Cognition
6 Tom Everitt, Victoria Krakovna, Laurent Orseau, Marcus Hutter and Shane Legg. Value Learning from a Corrupted Signal
7 John Fox. Slicing and dicing AI theories: how close are we to an agreed ontology?
8 Sam Freed. Is All Original Programming Introspective?
9 Arzu Gokmen. Institutional Facts and AI in Society
10 Jodi Guazzini. A Gnoseological Approach to the SGP: the Difference between Perception and Knowledge and Two Ways of Being Meaningful
11 Mahi Hardalupas. On a new "systematic" account of machine moral agency
12 Soheil Human, Golnaz Bidabadi and Vadim Savenkov. Supporting Pluralism by Artificial Intelligence: Conceptualizing Epistemic Disagreements As Digital Artifacts
13 Soheil Human, Markus Peschl, Golnaz Bidabadi and Vadim Savenkov. An Enactive Theory of Need Satisfaction
14 Thomas Kane. Dealing with Artificial Persons and Four Types of Artificial Intelligence
15 Yoshihiro Maruyama. Pancomputationalism and Philosophy of Data Science: From Symbolic to Statistical AI, and to Quantum AI?
16 Dagmar Monett and Colin Lewis. Getting clarity by defining Artificial Intelligence — A survey
17 Caterina Moruzzi.  Creative AI: Music Composition Programs as an Extension of the Composer's Mind
18 Stefan Reining. Revisiting the Dancing-Qualia Argument for Computationalism
19 Aziz F. Zambak and Erdem Unal. Computational Discovery Models: A Category Theoretic Approach to Knowledge Representation in Science
20 Carlos Zednik. From Machine Learning to Machine Intelligence
  List of Accepted Poster Presentations: Abstracts  
1 Wolfhart Totschnig. Fully autonomous AI Abstract: In the field of AI, the term “autonomy” is generally used to refer to the capacity of an artificial agent to operate independently of human oversight in complex environments. In philosophy, by contrast, the term “autonomy” is generally used to refer to a stronger capacity, namely the capacity to “give oneself the law,” i.e., to decide by oneself what one’s goal or principle of action will be. The predominant view in the literature on the long-term prospects and risks of artificial intelligence is that an artificial agent cannot exhibit autonomy of this kind because it cannot rationally change its own final goal. The aim of the present paper is to challenge this view by showing that it is based on questionable assumptions about the behavior of intelligent agents.
2 Paulius Astromskis. In Critique of RoboLaw: the Model of SmartLaw Abstract: The exponential development of the intelligent technologies requires in depth analysis of the ethical and legal issues raised by their applications. The existing regulation model and the very idea that laws should be made on robots should not be taken as granted anymore. Besides the laws on robots, there are emerging alternatives such as laws by robots and laws in robots. Accordingly, in the search of the ways to reconcile regulation and technology, the transaction cost analysis of the existing regulation model per se, in the context of technological singularity, should be performed. After such analysis is completed, one can identify the questions to be answered in the search of the trust-free model of regulation (i.e. the model of SmartLaw)
3 Selmer Bringsjord and Naveen Sundar Govindarajulu. Do Machine-Learning Machines Learn? Abstract: No; despite the Zeitgeist, according to which vaunted `ML' is on the brink of disemploying most members of H. sapiens sapiens, no.  Were the correct answer `Yes,' a machine that machine-learns some target t would, in a determinate, non-question-begging sense of `learn,' learn t.  But this cannot be the case. Why?  Because an effortless application of the process of elimination, a.k.a. disjunctive syllogism, proves the negative reply.  We use proof by cases.  In the first case, the unary number-theoretic function g learned by a human is Turing-uncomputable --- which entails that no standard artificial neural network can machine-learn g.  In Case 2, g is Turing-computable, but, for reasons we explain, not machine-learnable.  Our case includes a defense of a modern, limited version of ordinary-language philosophy.
4 Christopher Burr, Nello Cristianini and James Ladyman. Intelligent Agents and the Manipulation of User Behaviour Abstract: There are many ways in which autonomous software agents can affect the behaviour of their users, either directly or indirectly. We describe the most common examples, using the standard model of bounded rationality as an organising principle. We then focus on the particular case in which the utility function pursued by the software agent is defined in terms of the user’s actions: in this case the agent can increase its utility by reducing the autonomy of the user, but need not always do so. We discuss the cases where user behaviour is influenced without changing their utility function, by exploiting (and sometimes reducing) existing limitations of the decision making process. Finally we discuss the implications of persuasive technologies for human autonomy, particularly the case where personal information is used by the agent to determine how it interacts with the user.
5 Gordana Dodig Crnkovic. Morphologically Computing Embodied, Embedded, Enactive, Extended Cognition Abstract: Cognitive science in The Stanford Encyclopedia of Philosophy is considered to be the study of mind and intelligence, developed through interdisciplinary collaboration between psychology and philosophy of mind, linguistics, neuroscience, anthropology and artificial intelligence (Thagard, 2014). Under such narrow definition of cognitive science variety of unsolved/unsolvable problems appear. Much can be won by broadening the definition of cognition, to include sub-symbolic processes in humans (i.e. feelings, intuitions), to involve cognition in other living beings and distributed social cognition. This is done by connecting cognitivists and EEEE (embodied, embedded, enactive, extended) approaches through the idea of morphological computation as info-computational processing in cognizing agents at variety of levels of organisation, emerging through evolution of organisms in interaction with the environment.
6 Tom Everitt, Victoria Krakovna, Laurent Orseau, Marcus Hutter and Shane Legg. Value Learning from a Corrupted Signal Abstract: Sensory errors, software bugs, or reward misspecifications may incentivise agents to cheat. For example, a reinforcement learning agent may prefer states where a sensory error gives it the maximum reward, but where its not doing anything useful. This problem can be formalised in a generalised Markov Decision Process. We study the performance of traditional RL methods, well-intentioned agents designed to manage reward corruption, randomised agents, and agents using richer sources of data such as in inverse RL. The main takeaways are that inverse RL is safer than RL, and that randomisation may improve robustness in settings where only a reward signal is available.
7 John Fox. Slicing and dicing AI theories: how close are we to an agreed ontology? Abstract: In 1994 Allen Newell, one of the founders of AI and admired by psychologists and computer scientists alike, proposed what we would now call a grand challenge for cognitive science, the development of a "Unified Theory of Cognition". Since then, however, interdisciplinary research on UTC has fallen away and the separation of AI, computer science and psychology have been worsened by the fragmentation of research into countless conceptual and methodological silos. This talk will discuss how lack of a common vocabulary and theoretical ontology raises obstacles to addressing Newell’s grand challenge and outlines a possible way forward.
8 Sam Freed. Is All Original Programming Introspective? Abstract: Introspection has had a bad press in cognitive science. In a recent publication, introspection was rehabilitated as a source of ideas for AI development. This talk will explore the possibility that all original programming, AI or not, requires using of introspection. This is shown by exploring role-playing, how when pretending to be in a world consisting of a software environment (say python), introspection is how we explore the possibilities to attain our goes as programmers.
9 Arzu Gokmen. Institutional Facts and AI in Society Abstract: This study is an ethical assessment of design of the infosphere by considering the human beings not as beneficiaries or consumers of ICT’s but as the source of the data that a machine cannot learn otherwise. The crucial success point of intelligent systems is that they learn from data. But, what is the source of data about social reality? As Searle points out, unlike the brute facts about physical world, the ontology of the human facts, which he calls institutional facts, is subjective and they exist within a social environment. And the appropriate way to learn these facts is by interaction. But, how should this interaction be like, and with whom, before AI became ‘mature’?; and this implies that we face almost the same problem with raising a child.
10 Jodi Guazzini. A Gnoseological Approach to the SGP: the Difference between Perception and Knowledge and Two Ways of Being Meaningful Abstract: I identify two ways in which representations can acquire meaning within human knowledge and argue that one of these can be a partial solution to the “Symbol Grounding Problem”. The root of the SGP is that representations generated by an entity that has no immediate grasp on the world must receive meanings from objects. Since both human body/mind and AI are concerned by this, I suggest that an analysis of human ways of knowing may suggest a viable strategy for grounding meaning in symbolic writings used in AI. The solution I will present is to accept as a model symbolic writings which can construct their objects by translating their description into a system of reference within which it is possible to specify both how the translation has been developed, and how the resulting description of the referent can be validated.
11 Mahi Hardalupas. On a new "systematic" account of machine moral agency Abstract: I present a new approach to moral agency, which I argue is more suitable for analyzing machine moral agency than the traditional account. First, I outline the traditional account of moral agency and, by considering two thought experiments, show why it is flawed when applied to machine moral agency and other cases. Then, I present an alternative “systematic” account of moral agency and apply it to paradigmatic cases. In this new account, though machines alone cannot be moral agents, they can be partial moral agents in a system, where the system is a moral agent. Finally, I address potential challenges to this new account and explain how the systematic account is equipped to address them.
12 Soheil Human, Golnaz Bidabadi and Vadim Savenkov. Supporting Pluralism by Artificial Intelligence: Conceptualizing Epistemic Disagreements As Digital Artifacts Abstract: A crucial concept in philosophy and social sciences, epistemic disagreement, has not yet been adequately reflected in the Web. We argue that intelligent tools for detection, representation and visualisation of epistemic disagreements are needed to support pluralism. As a first step, epistemic disagreements and possible responses to them are conceptualised and an ontology for representing and annotating disagreements is proposed.  Potential applications, challenges and future works are discussed.
13 Soheil Human, Markus Peschl, Golnaz Bidabadi and Vadim Savenkov. An Enactive Theory of Need Satisfaction Abstract: Need satisfaction can be considered as one of the most fundamental aspects of biological cognitive agents. The problem of need satisfaction is defined as "how is an object or a state inferred to be a satisfier for a cognitive agent[’s need(s)]?". In this paper, based on the interdisciplinary literature on need satisfaction and state of the art evidence and theories in cognitive science and artificial intelligence, including predictive processing, an important and emerging approach that views the brain as a hypothesis-testing mechanism, an enactive theory of need satisfaction is presented. Besides its potential contribution to better understanding of biological cognitive systems, the proposed cognitive theory can be seen as a first step towards development of enactive need-oriented artificial agents.
14 Thomas Kane. Dealing with Artificial Persons and Four Types of Artificial Intelligence Abstract: The intelligence (organisational intelligence) of organisations (artificial persons) may be the most successful form of artificial intelligence that is operational in the world today.  In companies such as Facebook, it has already demonstrated capabilities for altering human behaviours. We present a new means of analysis for this type of intelligence and suggest new means of reasoning with it. The paper presents an Artificial Person within Hobbesian terminology and adapts Heidegger’s ontological framework to introduce a level 2.x being, with which  algorithmic, professional, organisational and societal artificial intelligences can be positioned, and from which new forms of Chinese Rooms, useful for monitoring and curbing inappropriate behavior in organisations could be developed.   
15 Yoshihiro Maruyama. Pancomputationalism and Philosophy of Data Science: From Symbolic to Statistical AI, and to Quantum AI? Abstract: The rise of probability and statistics is striking in contemporary science, ranging from physics to artificial intelligence. Here we focus upon two issues in particular: one is the computational theory of mind as the fundamental underpinning of AI, and the nature of computation there; the other is the transition from symbolic to statistical AI, and the nature of truth in data science as a new kind of science. We argue: "computation" in the computational theory of mind must ultimately be quantum if the singularity thesis is true; data science is concerned with a new form of scientific truth, which may be called "post-truth"; whereas conventional science is about establishing universal truths from pure data carefully collected in a controlled situation, data science is about indicating useful, existential truths from real-world data collected from contingent real-life and contaminated in different ways.
16 Dagmar Monett and Colin Lewis. Getting clarity by defining Artificial Intelligence — A survey Abstract: We present the preliminary results of our research survey “Defining (machine) Intelligence.” The aim of the survey is to gather opinions, from a cross sector of professionals, ultimately to help create a unified message on the goal and definition of Artificial Intelligence (A.I.). The survey on definitions of machine and human intelligence is still accepting responses. There has been a positive volume of responses together with high-level, opinions and recommendations concerning the definitions from experts around the world. We hope we can contribute to the science of A.I. with a well-defined goal of the discipline and also spread a stronger, more coherent message, to the mainstream media, policymakers, investors, and the general public to help dispel myths about A.I.
17 Caterina Moruzzi.  Creative AI: Music Composition Programs as an Extension of the Composer's Mind Abstract: In this paper I answer the question ‘Can a computer be creative?’ by focusing on music as paradigmatic expression of human creativity. The diffusion of AI music generation programs raises the concern of whether they produce ‘musical works’. A widely recognised requirement of musical works is that of being intentionally created. It follows that AI music programs produce ‘musical works’ only if they are intentionally creative. My central claim is that AI music generators possess creativity insofar as they are an extension of the musician’s mind. More generally, I argue that, even though they are located outside of the human’s head, AI programs are integrated into the cognitive process that leads to the production of expressions of creativity.
18 Stefan Reining. Revisiting the Dancing-Qualia Argument for Computationalism Abstract: My aim in this talk will be to attack David Chalmers’ dancing-qualia argument for computational sufficiency from a hitherto neglected angle. Chalmers’ argument involves the claim that if replacing certain neurons with input/output-equivalent silicon chips resulted in a modification of the subject’s phenomenal state, then the subject should be able to notice the change. I will, however, show that this claim is incompatible with a well-established view in neurobiology regarding the workings of phenomenal memory, according to which remembering phenomenal states involves a reactivation of the very same neurons that were active during the original perceptual episode, such that neuronal replacement also alters the subject’s phenomenal memory and the modification would therefore indeed go unnoticed by the subject.
19 Aziz F. Zambak and Erdem Unal. Computational Discovery Models: A Category Theoretic Approach to Knowledge Representation in Science Abstract: Data, information and knowledge are exponentially growing in natural, formal, and social sciences. The growth of data and knowledge brought novel topics into AI such as big data, semantic web, machine learning etc. We claim that the very old AI problem namely, knowledge representation, is at the intersection of all these novel topics. The classical theories in knowledge representation are insufficient to provide a new perspective to these novel topics of AI. This paper aims at showing that we need a new approach to knowledge representation based on the category theory. We will propose the Uni-Morphic Mapping as a computational ontology and the HWSL [HypoWoven Source Language] as a Mark-up Language for a proper knowledge representation model that can be used in developing AI techniques that can contribute to develop the theoretic/hypothetic content of scientific knowledge.  
20 Carlos Zednik. From Machine Learning to Machine Intelligence Abstract: In this talk I consider the prospects of Machine Learning methods such as Deep Learning to develop intelligent computers. To this end, I outline a generalized Turing Test in which computers are tasked with exhibiting intelligent behavior in a variety of contexts, but also urge for the importance of “looking under the hood”. Unfortunately, “looking under the hood” is notoriously difficult—the Black Box Problem in AI. I consider the nascent Explainable AI research program as a possible solution to this problem, but also provide independent reasons for thinking that Machine Learning will yield computers that act like humans, and that act for the same kinds of reasons. Because these computers are being nurtured and situated in the real-world environment also inhabited by humans, the similarities between human and artificial intelligence will be more than skin deep.