68,560 research outputs found
Multi-Agent Only-Knowing Revisited
Levesque introduced the notion of only-knowing to precisely capture the
beliefs of a knowledge base. He also showed how only-knowing can be used to
formalize non-monotonic behavior within a monotonic logic. Despite its appeal,
all attempts to extend only-knowing to the many agent case have undesirable
properties. A belief model by Halpern and Lakemeyer, for instance, appeals to
proof-theoretic constructs in the semantics and needs to axiomatize validity as
part of the logic. It is also not clear how to generalize their ideas to a
first-order case. In this paper, we propose a new account of multi-agent
only-knowing which, for the first time, has a natural possible-world semantics
for a quantified language with equality. We then provide, for the propositional
fragment, a sound and complete axiomatization that faithfully lifts Levesque's
proof theory to the many agent case. We also discuss comparisons to the earlier
approach by Halpern and Lakemeyer.Comment: Appears in Principles of Knowledge Representation and Reasoning 201
Relating Knowledge and Coordinated Action: The Knowledge of Preconditions Principle
The Knowledge of Preconditions principle (KoP) is proposed as a widely
applicable connection between knowledge and action in multi-agent systems.
Roughly speaking, it asserts that if some condition is a necessary condition
for performing a given action A, then knowing that this condition holds is also
a necessary condition for performing A. Since the specifications of tasks often
involve necessary conditions for actions, the KoP principle shows that such
specifications induce knowledge preconditions for the actions. Distributed
protocols or multi-agent plans that satisfy the specifications must ensure that
this knowledge be attained, and that it is detected by the agents as a
condition for action. The knowledge of preconditions principle is formalised in
the runs and systems framework, and is proven to hold in a wide class of
settings. Well-known connections between knowledge and coordinated action are
extended and shown to derive directly from the KoP principle: a "common
knowledge of preconditions" principle is established showing that common
knowledge is a necessary condition for performing simultaneous actions, and a
"nested knowledge of preconditions" principle is proven, showing that
coordinating actions to be performed in linear temporal order requires a
corresponding form of nested knowledge.Comment: In Proceedings TARK 2015, arXiv:1606.0729
Bounded regret in stochastic multi-armed bandits
We study the stochastic multi-armed bandit problem when one knows the value
of an optimal arm, as a well as a positive lower bound on the
smallest positive gap . We propose a new randomized policy that attains
a regret {\em uniformly bounded over time} in this setting. We also prove
several lower bounds, which show in particular that bounded regret is not
possible if one only knows , and bounded regret of order is
not possible if one only knows $\mu^{(\star)}
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Theorems and unawareness
This paper provides a set-theoretic model of knowledge and unawareness, in which reasoning through theorems is employed. A new property called Awareness Leads to Knowledge shows that unawareness of theorems not only constrains an agent's knowledge, but also, can impair his reasoning about what other agents know. For example, in contrast to Li (2006), Heifetz, Meier, and Schipper (2006) and the standard model of knowledge, it is possible that two agents disagree on whether another agent knows a particular event
Reclaiming human machine nature
Extending and modifying his domain of life by artifact production is one of
the main characteristics of humankind. From the first hominid, who used a wood
stick or a stone for extending his upper limbs and augmenting his gesture
strength, to current systems engineers who used technologies for augmenting
human cognition, perception and action, extending human body capabilities
remains a big issue. From more than fifty years cybernetics, computer and
cognitive sciences have imposed only one reductionist model of human machine
systems: cognitive systems. Inspired by philosophy, behaviorist psychology and
the information treatment metaphor, the cognitive system paradigm requires a
function view and a functional analysis in human systems design process.
According that design approach, human have been reduced to his metaphysical and
functional properties in a new dualism. Human body requirements have been left
to physical ergonomics or "physiology". With multidisciplinary convergence, the
issues of "human-machine" systems and "human artifacts" evolve. The loss of
biological and social boundaries between human organisms and interactive and
informational physical artifact questions the current engineering methods and
ergonomic design of cognitive systems. New developpment of human machine
systems for intensive care, human space activities or bio-engineering sytems
requires grounding human systems design on a renewed epistemological framework
for future human systems model and evidence based "bio-engineering". In that
context, reclaiming human factors, augmented human and human machine nature is
a necessityComment: Published in HCI International 2014, Heraklion : Greece (2014
Modeling the mobility of living organisms in heterogeneous landscapes: Does memory improve foraging success?
Thanks to recent technological advances, it is now possible to track with an
unprecedented precision and for long periods of time the movement patterns of
many living organisms in their habitat. The increasing amount of data available
on single trajectories offers the possibility of understanding how animals move
and of testing basic movement models. Random walks have long represented the
main description for micro-organisms and have also been useful to understand
the foraging behaviour of large animals. Nevertheless, most vertebrates, in
particular humans and other primates, rely on sophisticated cognitive tools
such as spatial maps, episodic memory and travel cost discounting. These
properties call for other modeling approaches of mobility patterns. We propose
a foraging framework where a learning mobile agent uses a combination of
memory-based and random steps. We investigate how advantageous it is to use
memory for exploiting resources in heterogeneous and changing environments. An
adequate balance of determinism and random exploration is found to maximize the
foraging efficiency and to generate trajectories with an intricate
spatio-temporal order. Based on this approach, we propose some tools for
analysing the non-random nature of mobility patterns in general.Comment: 14 pages, 4 figures, improved discussio
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