A Combined System for Update Logic and Belief Revision

Revised Selected PapersInternational audienceIn this paper we propose a logical system combining the update logic of A. Baltag, L. Moss and S. Solecki (to which we will refer to by the generic term BMS, [BMS04]) with the belief revision theory as conceived by C. Alchourron, P. Gardenfors and D. Mackinson (that we will call the AGM theory, [GardRott95]) viewed from the point of view of W. Spohn ( [Spohn90], [Spohn88]). We also give a proof system and a comparison with the AGM postulates

Modelling Social Structures and Hierarchies in Language Evolution

Language evolution might have preferred certain prior social configurations over others. Experiments conducted with models of different social structures (varying subgroup interactions and the role of a dominant interlocutor) suggest that having isolated agent groups rather than an interconnected agent is more advantageous for the emergence of a social communication system. Distinctive groups that are closely connected by communication yield systems less like natural language than fully isolated groups inhabiting the same world. Furthermore, the addition of a dominant male who is asymmetrically favoured as a hearer, and equally likely to be a speaker has no positive influence on the disjoint groups.Comment: 14 pages, 3 figures, 1 table. In proceedings of AI-2010, The Thirtieth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, 14-16 December 201

Social Welfare in One-sided Matching Markets without Money

We study social welfare in one-sided matching markets where the goal is to efficiently allocate n items to n agents that each have a complete, private preference list and a unit demand over the items. Our focus is on allocation mechanisms that do not involve any monetary payments. We consider two natural measures of social welfare: the ordinal welfare factor which measures the number of agents that are at least as happy as in some unknown, arbitrary benchmark allocation, and the linear welfare factor which assumes an agent's utility linearly decreases down his preference lists, and measures the total utility to that achieved by an optimal allocation. We analyze two matching mechanisms which have been extensively studied by economists. The first mechanism is the random serial dictatorship (RSD) where agents are ordered in accordance with a randomly chosen permutation, and are successively allocated their best choice among the unallocated items. The second mechanism is the probabilistic serial (PS) mechanism of Bogomolnaia and Moulin [8], which computes a fractional allocation that can be expressed as a convex combination of integral allocations. The welfare factor of a mechanism is the infimum over all instances. For RSD, we show that the ordinal welfare factor is asymptotically 1/2, while the linear welfare factor lies in the interval [.526, 2/3]. For PS, we show that the ordinal welfare factor is also 1/2 while the linear welfare factor is roughly 2/3. To our knowledge, these results are the first non-trivial performance guarantees for these natural mechanisms

Event structure, conceptual spaces and the semantics of verbs

The aim of this paper is to integrate spatial cognition with lexical semantics. We develop cognitive models of actions and events based on conceptual spaces and vectors on them. The models are then used to present a semantic theory of verbs. We propose a two-vector model of events including a force vector and a result vector. We argue that our framework provides a unified account of a multiplicity of linguistic phenomena related to verbs. Among others it provides a cognitive explanation of the lexical constraint regarding manner vs. result and polysemy caused by intentionality. It also generates a unified definition of aspect

Belief Revision in Structured Probabilistic Argumentation

In real-world applications, knowledge bases consisting of all the information at hand for a specific domain, along with the current state of affairs, are bound to contain contradictory data coming from different sources, as well as data with varying degrees of uncertainty attached. Likewise, an important aspect of the effort associated with maintaining knowledge bases is deciding what information is no longer useful; pieces of information (such as intelligence reports) may be outdated, may come from sources that have recently been discovered to be of low quality, or abundant evidence may be available that contradicts them. In this paper, we propose a probabilistic structured argumentation framework that arises from the extension of Presumptive Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue that this formalism is capable of addressing the basic issues of handling contradictory and uncertain data. Then, to address the last issue, we focus on the study of non-prioritized belief revision operations over probabilistic PreDeLP programs. We propose a set of rationality postulates -- based on well-known ones developed for classical knowledge bases -- that characterize how such operations should behave, and study a class of operators along with theoretical relationships with the proposed postulates, including a representation theorem stating the equivalence between this class and the class of operators characterized by the postulates

Directing human attention with pointing

© 2014 IEEE. Pointing is a typical means of directing a human's attention to a specific object or event. Robot pointing behaviours that direct the attention of humans are critical for human-robot interaction, communication and collaboration. In this paper, we describe an experiment undertaken to investigate human comprehension of a humanoid robot's pointing behaviour. We programmed a NAO robot to point to markers on a large screen and asked untrained human subjects to identify the target of the robots pointing gesture. We found that humans are able to identify robot pointing gestures. Human subjects achieved higher levels of comprehension when the robot pointed at objects closer to the gesturing arm and when they stood behind the robot. In addition, we found that subjects performance improved with each assessment task. These new results can be used to guide the design of effective robot pointing behaviours that enable more effective robot to human communication and improve human-robot collaborative performance

Converting Endangered Species Categories to Probabilities of Extinction for Phylogenetic Conservation Prioritization

Categories of imperilment like the global IUCN Red List have been transformed to probabilities of extinction and used to rank species by the amount of imperiled evolutionary history they represent (e.g. by the Edge of Existence programme). We investigate the stability of such lists when ranks are converted to probabilities of extinction under different scenarios.Using a simple example and computer simulation, we show that preserving the categories when converting such list designations to probabilities of extinction does not guarantee the stability of the resulting lists.Care must be taken when choosing a suitable transformation, especially if conservation dollars are allocated to species in a ranked fashion. We advocate routine sensitivity analyses

Moral Dilemmas for Artificial Intelligence: a position paper on an application of Compositional Quantum Cognition

Traditionally, the way one evaluates the performance of an Artificial Intelligence (AI) system is via a comparison to human performance in specific tasks, treating humans as a reference for high-level cognition. However, these comparisons leave out important features of human intelligence: the capability to transfer knowledge and make complex decisions based on emotional and rational reasoning. These decisions are influenced by current inferences as well as prior experiences, making the decision process strongly subjective and apparently biased. In this context, a definition of compositional intelligence is necessary to incorporate these features in future AI tests. Here, a concrete implementation of this will be suggested, using recent developments in quantum cognition, natural language and compositional meaning of sentences, thanks to categorical compositional models of meaning.Comment: 15 pages, 3 figures, Conference paper at Quantum Interaction 2018, Nice, France. Published in Lecture Notes in Computer Science, vol 11690, Springer, Cham. Online ISBN 978-3-030-35895-