8,584 research outputs found
Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework
In this paper, we argue that the future of Artificial Intelligence research
resides in two keywords: integration and embodiment. We support this claim by
analyzing the recent advances of the field. Regarding integration, we note that
the most impactful recent contributions have been made possible through the
integration of recent Machine Learning methods (based in particular on Deep
Learning and Recurrent Neural Networks) with more traditional ones (e.g.
Monte-Carlo tree search, goal babbling exploration or addressable memory
systems). Regarding embodiment, we note that the traditional benchmark tasks
(e.g. visual classification or board games) are becoming obsolete as
state-of-the-art learning algorithms approach or even surpass human performance
in most of them, having recently encouraged the development of first-person 3D
game platforms embedding realistic physics. Building upon this analysis, we
first propose an embodied cognitive architecture integrating heterogenous
sub-fields of Artificial Intelligence into a unified framework. We demonstrate
the utility of our approach by showing how major contributions of the field can
be expressed within the proposed framework. We then claim that benchmarking
environments need to reproduce ecologically-valid conditions for bootstrapping
the acquisition of increasingly complex cognitive skills through the concept of
a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017
conference (Lisbon, Portugal
Integrating planning and reactive control
Artificial intelligence research on planning is concerned with designing control systems that choose actions by manipulating explicit descriptions of the world state, the goal to be achieved, and the effects of elementary operations available to the system. Because planning shifts much of the burden of reasoning to the machine, it holds great appeal as a high-level programming method. Experience shows, however, that it cannot be used indiscriminately because even moderately rich languages for describing goals, states, and the elementary operators lead to computational inefficiencies that render the approach unsuitable for realistic applications. This inadequacy has spawned a recent wave of research on reactive control or situated activity in which control systems are modeled as reacting directly to the current situation rather than as reasoning about the future effects of alternative action sequences. While this research has confronted the issue of run-time tractability head on, in many cases it has done so by sacrificing the advantages of declarative planning techniques. Ways in which the two approaches can be unified are discussed. The authors begin by modeling reactive control systems as state machines that map a stream of sensory inputs to a stream of control outputs. These machines can be decomposed into two continuously active subsystems: the planner and the execution module. The planner computes a plan, which can be seen as a set of bits that control the behavior of the execution module. An important element of this work is the formulation of a precise semantic interpretation for the inputs and outputs of the planning system. They show that the distinction between planned and reactive behavior is largely in the eye of the beholder: systems that seem to compute explicit plans can be redescribed in situation-action terms and vice versa. They also discuss practical programming techniques that allow the advantages of declarative programming and guaranteed reactive response to be achieved simultaneously
Verifying the Interplay of Authorization Policies and Workflow in Service-Oriented Architectures (Full version)
A widespread design approach in distributed applications based on the
service-oriented paradigm, such as web-services, consists of clearly separating
the enforcement of authorization policies and the workflow of the applications,
so that the interplay between the policy level and the workflow level is
abstracted away. While such an approach is attractive because it is quite
simple and permits one to reason about crucial properties of the policies under
consideration, it does not provide the right level of abstraction to specify
and reason about the way the workflow may interfere with the policies, and vice
versa. For example, the creation of a certificate as a side effect of a
workflow operation may enable a policy rule to fire and grant access to a
certain resource; without executing the operation, the policy rule should
remain inactive. Similarly, policy queries may be used as guards for workflow
transitions.
In this paper, we present a two-level formal verification framework to
overcome these problems and formally reason about the interplay of
authorization policies and workflow in service-oriented architectures. This
allows us to define and investigate some verification problems for SO
applications and give sufficient conditions for their decidability.Comment: 16 pages, 4 figures, full version of paper at Symposium on Secure
Computing (SecureCom09
Ethics of Artificial Intelligence Demarcations
In this paper we present a set of key demarcations, particularly important
when discussing ethical and societal issues of current AI research and
applications. Properly distinguishing issues and concerns related to Artificial
General Intelligence and weak AI, between symbolic and connectionist AI, AI
methods, data and applications are prerequisites for an informed debate. Such
demarcations would not only facilitate much-needed discussions on ethics on
current AI technologies and research. In addition sufficiently establishing
such demarcations would also enhance knowledge-sharing and support rigor in
interdisciplinary research between technical and social sciences.Comment: Proceedings of the Norwegian AI Symposium 2019 (NAIS 2019),
Trondheim, Norwa
Connectionist Inference Models
The performance of symbolic inference tasks has long been a challenge to connectionists. In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rule-based reasoning, and whether they involve distributed or localist representations. The benefits and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used for cognitive modeling
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