The intentional stance is the strategy of prediction and explanation that attributes beliefs, desires, and other states to systems and predicts future behavior from what it would be rational for an agent to do, given those beliefs and desires. Any system whose performance can be thus predicted and explained is an intentional system, whatever its innards. The strategy of treating parts of the world as intentional systems is the foundation of but is also exploited in artificial intelligence and cognitive science (...) more generally, as well as in evolutionary theory. An analysis of the role of the intentional stance and its presuppositions supports a naturalistic theory of mental states and events, their content or intentionality, and the relation between levels of explanation and neurophysiological or mechanistic levels of explanation. As such, the analysis of the intentional stance grounds a theory of the mind and its relation to the body. (shrink)

John Searle’s Chinese room argument is a celebrated thought experiment designed to refute the hypothesis, popular among artificial intelligence scientists and philosophers of mind, that “the appropriately programmed computer really is a mind”. Since its publication in 1980, the CRA has evoked an enormous amount of debate about its implications for machine intelligence, the functionalist philosophy of mind, theories of consciousness, etc. Although the general consensus among commentators is that the CRA is flawed, and not withstanding the popularity of the (...) systems reply in some quarters, there is remarkably little agreement on exactly how and why it is flawed. A newcomer to the controversy could be forgiven for thinking that the bewildering collection of diverse replies to Searle betrays a tendency to unprincipled, ad hoc argumentation and, thereby, a weakness in the opposition’s case. In this paper, treating the CRA as a prototypical example of a ‘destructive’ thought experiment, I attempt to set it in a logical framework, which allows us to systematise and classify the various objections. Since thought experiments are always posed in narrative form, formal logic by itself cannot fully capture the controversy. On the contrary, much also hinges on how one translates between the informal everyday language in which the CRA was initially framed and formal logic and, in particular, on the specific conception of possibility that one reads into the logical formalism. (shrink)

The term “Contemplative sciences” refers to an interdisciplinary approach to mind that aims at a better understanding of alternative states of consciousness, like those obtained trough deep concentration and meditation, mindfulness and other “superior” or “spiritual” mental states. There is, however, a key discipline missing: artificial intelligence. AI has forgotten its original aims to create intelligent machines that could help us to understand better what intelligence is and is more worried about pragmatical stuff, so almost nobody in the field seems (...) to be interested to join this new effort of contemplative science. In this paper, I would like to accomplish the following: (1) To give a brief description of the field of “contemplative sciences;” (2) To argue why AI should actively join this new paradigm on the study of the mind; and (3) To set up a research program on artificial wisdom: that is to design computational systems that can model at least some relevant aspects of human wisdom. (shrink)

We propose a framework for modelling situated resource-bounded agents. The framework is based on an objective ascription of intentional modalities and can be easily tailored to the system we want to model and the properties we wish to specify. As an elaboration of the framework, we introduce a logic, OBA, for describing the observations, beliefs, goals and actions of simple agents, and show that OBA is complete, decidable and has an efficient model checking procedure, allowing properties of agents specified in (...) OBA to be verified using standard theorem proving or model checking techniques. (shrink)

What psychological and philosophical significance should we attach to recent efforts at computer simulations of human cognitive capacities? In answering this question, I find it useful to distinguish what I will call "strong" AI from "weak" or "cautious" AI. According to weak AI, the principal value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous and precise fashion. (...) But according to strong AI, the computer is not merely a tool in the study of the mind; rather, the appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to. (shrink)

This book reports on the results of the third edition of the premier conference in the field of philosophy of artificial intelligence, PT-AI 2017, held on November 4 - 5, 2017 at the University of Leeds, UK. It covers: advanced knowledge on key AI concepts, including complexity, computation, creativity, embodiment, representation and superintelligence; cutting-edge ethical issues, such as the AI impact on human dignity and society, responsibilities and rights of machines, as well as AI threats to humanity and AI safety; (...) and cutting-edge developments in techniques to achieve AI, including machine learning, neural networks, dynamical systems. The book also discusses important applications of AI, including big data analytics, expert systems, cognitive architectures, and robotics. It offers a timely, yet very comprehensive snapshot of what is going on in the field of AI, especially at the interfaces between philosophy, cognitive science, ethics and computing. (shrink)

It is argued thatde dicto andde re beliefs are attitudes towards syntactically structured entities (sentences) in the head. In order to identify the content of ade dicto orde re belief, we must be able to match causal relations of belief states to natural language inferences. Such match-ups provide sufficient empirical justification for regarding those causal relations as syntactic transformations, that is, inferences. But only syntactically structured entities are capable of enjoying such inferential relations. Hence,de dicto andde re beliefs must be (...) syntactically structured. Given that beliefs are also brain states, it follows that they are sentences in the brain. The argument presented here is shown to be an improvement over similar arguments advanced by Harman and Fodor. (shrink)

It is argued thatde dicto andde re beliefs are attitudes towards syntactically structured entities (sentences) in the head. In order to identify the content of ade dicto orde re belief, we must be able to match causal relations of belief states to natural language inferences. Such match-ups provide sufficient empirical justification for regarding those causal relations as syntactic transformations, that is, inferences. But only syntactically structured entities are capable of enjoying such inferential relations. Hence,de dicto andde re beliefs must be (...) syntactically structured. Given that beliefs are also brain states, it follows that they are sentences in the brain. The argument presented here is shown to be an improvement over similar arguments advanced by Harman and Fodor. (shrink)

In the last decade the concept of context has been extensivelyexploited in many research areas, e.g., distributed artificialintelligence, multi agent systems, distributed databases, informationintegration, cognitive science, and epistemology. Three alternative approaches to the formalization of the notion ofcontext have been proposed: Giunchiglia and Serafini's Multi LanguageSystems (ML systems), McCarthy's modal logics of contexts, andGabbay's Labelled Deductive Systems.Previous papers have argued in favor of ML systems with respect to theother approaches. Our aim in this paper is to support these arguments froma (...) theoretical perspective. We provide a very general definition of ML systems, which covers allthe ML systems used in the literature, and we develop a proof theoryfor an important subclass of them: the MR systems. We prove variousimportant results; among other things, we prove a normal form theorem,the sub-formula property, and the decidability of an importantinstance of the class of the MR systems. The paper concludes with a detailed comparison among the alternativeapproaches. (shrink)

Making interactions between humans and artificial agents successful is a major goal of interaction design. The aim of this paper is to provide researchers conducting interaction studies a new framework for the evaluation of robot believability. By critically examining the ordinary sense of believability, we first argue that currently available notions of it are underspecified for rigorous application in an experimental setting. We then define four concepts that capture different senses of believability, each of which connects directly to an empirical (...) methodology. Finally, we show how this framework has been and can be used in the construction of interaction studies by applying it to our own work in human–robot interaction. (shrink)