26,913 research outputs found
Declarative vs Rule-based Control for Flocking Dynamics
The popularity of rule-based flocking models, such as Reynolds' classic
flocking model, raises the question of whether more declarative flocking models
are possible. This question is motivated by the observation that declarative
models are generally simpler and easier to design, understand, and analyze than
operational models. We introduce a very simple control law for flocking based
on a cost function capturing cohesion (agents want to stay together) and
separation (agents do not want to get too close). We refer to it as {\textit
declarative flocking} (DF). We use model-predictive control (MPC) to define
controllers for DF in centralized and distributed settings. A thorough
performance comparison of our declarative flocking with Reynolds' model, and
with more recent flocking models that use MPC with a cost function based on
lattice structures, demonstrate that DF-MPC yields the best cohesion and least
fragmentation, and maintains a surprisingly good level of geometric regularity
while still producing natural flock shapes similar to those produced by
Reynolds' model. We also show that DF-MPC has high resilience to sensor noise.Comment: 7 Page
Extend Commitment Protocols with Temporal Regulations: Why and How
The proposal of Elisa Marengo's thesis is to extend commitment protocols to
explicitly account for temporal regulations. This extension will satisfy two
needs: (1) it will allow representing, in a flexible and modular way, temporal
regulations with a normative force, posed on the interaction, so as to
represent conventions, laws and suchlike; (2) it will allow committing to
complex conditions, which describe not only what will be achieved but to some
extent also how. These two aspects will be deeply investigated in the proposal
of a unified framework, which is part of the ongoing work and will be included
in the thesis.Comment: Proceedings of the Doctoral Consortium and Poster Session of the 5th
International Symposium on Rules (RuleML 2011@IJCAI), pages 1-8
(arXiv:1107.1686
Towards Verifiably Ethical Robot Behaviour
Ensuring that autonomous systems work ethically is both complex and
difficult. However, the idea of having an additional `governor' that assesses
options the system has, and prunes them to select the most ethical choices is
well understood. Recent work has produced such a governor consisting of a
`consequence engine' that assesses the likely future outcomes of actions then
applies a Safety/Ethical logic to select actions. Although this is appealing,
it is impossible to be certain that the most ethical options are actually
taken. In this paper we extend and apply a well-known agent verification
approach to our consequence engine, allowing us to verify the correctness of
its ethical decision-making.Comment: Presented at the 1st International Workshop on AI and Ethics, Sunday
25th January 2015, Hill Country A, Hyatt Regency Austin. Will appear in the
workshop proceedings published by AAA
Understanding ACT-R - an Outsider's Perspective
The ACT-R theory of cognition developed by John Anderson and colleagues
endeavors to explain how humans recall chunks of information and how they solve
problems. ACT-R also serves as a theoretical basis for "cognitive tutors",
i.e., automatic tutoring systems that help students learn mathematics, computer
programming, and other subjects. The official ACT-R definition is distributed
across a large body of literature spanning many articles and monographs, and
hence it is difficult for an "outsider" to learn the most important aspects of
the theory. This paper aims to provide a tutorial to the core components of the
ACT-R theory
Motivations, Values and Emotions: 3 sides of the same coin
This position paper speaks to the interrelationships between the three concepts of motivations, values, and emotion. Motivations prime actions, values serve to choose between motivations, emotions provide a common currency for values, and emotions implement motivations. While conceptually distinct, the three are so pragmatically intertwined as to differ primarily from our taking different points of view. To make these points more transparent, we briefly describe the three in the context a cognitive architecture, the LIDA model, for software agents and robots that models human cognition, including a developmental period. We also compare the LIDA model with other models of cognition, some involving learning and emotions. Finally, we conclude that artificial emotions will prove most valuable as implementers of motivations in situations requiring learning and development
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