17,275 research outputs found
Quantum speedup for active learning agents
Can quantum mechanics help us in building intelligent robots and agents? One
of the defining characteristics of intelligent behavior is the capacity to
learn from experience. However, a major bottleneck for agents to learn in any
real-life situation is the size and complexity of the corresponding task
environment. Owing to, e.g., a large space of possible strategies, learning is
typically slow. Even for a moderate task environment, it may simply take too
long to rationally respond to a given situation. If the environment is
impatient, allowing only a certain time for a response, an agent may then be
unable to cope with the situation and to learn at all. Here we show that
quantum physics can help and provide a significant speed-up for active learning
as a genuine problem of artificial intelligence. We introduce a large class of
quantum learning agents for which we show a quadratic boost in their active
learning efficiency over their classical analogues. This result will be
particularly relevant for applications involving complex task environments.Comment: Minor updates, 14 pages, 3 figure
A Projective Simulation Scheme for Partially-Observable Multi-Agent Systems
We introduce a kind of partial observability to the projective simulation
(PS) learning method. It is done by adding a belief projection operator and an
observability parameter to the original framework of the efficiency of the PS
model. I provide theoretical formulations, network representations, and
situated scenarios derived from the invasion toy problem as a starting point
for some multi-agent PS models.Comment: 28 pages, 21 figure
Post-Westgate SWAT : C4ISTAR Architectural Framework for Autonomous Network Integrated Multifaceted Warfighting Solutions Version 1.0 : A Peer-Reviewed Monograph
Police SWAT teams and Military Special Forces face mounting pressure and
challenges from adversaries that can only be resolved by way of ever more
sophisticated inputs into tactical operations. Lethal Autonomy provides
constrained military/security forces with a viable option, but only if
implementation has got proper empirically supported foundations. Autonomous
weapon systems can be designed and developed to conduct ground, air and naval
operations. This monograph offers some insights into the challenges of
developing legal, reliable and ethical forms of autonomous weapons, that
address the gap between Police or Law Enforcement and Military operations that
is growing exponentially small. National adversaries are today in many
instances hybrid threats, that manifest criminal and military traits, these
often require deployment of hybrid-capability autonomous weapons imbued with
the capability to taken on both Military and/or Security objectives. The
Westgate Terrorist Attack of 21st September 2013 in the Westlands suburb of
Nairobi, Kenya is a very clear manifestation of the hybrid combat scenario that
required military response and police investigations against a fighting cell of
the Somalia based globally networked Al Shabaab terrorist group.Comment: 52 pages, 6 Figures, over 40 references, reviewed by a reade
Can biological quantum networks solve NP-hard problems?
There is a widespread view that the human brain is so complex that it cannot
be efficiently simulated by universal Turing machines. During the last decades
the question has therefore been raised whether we need to consider quantum
effects to explain the imagined cognitive power of a conscious mind.
This paper presents a personal view of several fields of philosophy and
computational neurobiology in an attempt to suggest a realistic picture of how
the brain might work as a basis for perception, consciousness and cognition.
The purpose is to be able to identify and evaluate instances where quantum
effects might play a significant role in cognitive processes.
Not surprisingly, the conclusion is that quantum-enhanced cognition and
intelligence are very unlikely to be found in biological brains. Quantum
effects may certainly influence the functionality of various components and
signalling pathways at the molecular level in the brain network, like ion
ports, synapses, sensors, and enzymes. This might evidently influence the
functionality of some nodes and perhaps even the overall intelligence of the
brain network, but hardly give it any dramatically enhanced functionality. So,
the conclusion is that biological quantum networks can only approximately solve
small instances of NP-hard problems.
On the other hand, artificial intelligence and machine learning implemented
in complex dynamical systems based on genuine quantum networks can certainly be
expected to show enhanced performance and quantum advantage compared with
classical networks. Nevertheless, even quantum networks can only be expected to
efficiently solve NP-hard problems approximately. In the end it is a question
of precision - Nature is approximate.Comment: 38 page
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