81,339 research outputs found
A distributed agent architecture for real-time knowledge-based systems: Real-time expert systems project, phase 1
We propose a distributed agent architecture (DAA) that can support a variety of paradigms based on both traditional real-time computing and artificial intelligence. DAA consists of distributed agents that are classified into two categories: reactive and cognitive. Reactive agents can be implemented directly in Ada to meet hard real-time requirements and be deployed on on-board embedded processors. A traditional real-time computing methodology under consideration is the rate monotonic theory that can guarantee schedulability based on analytical methods. AI techniques under consideration for reactive agents are approximate or anytime reasoning that can be implemented using Bayesian belief networks as in Guardian. Cognitive agents are traditional expert systems that can be implemented in ART-Ada to meet soft real-time requirements. During the initial design of cognitive agents, it is critical to consider the migration path that would allow initial deployment on ground-based workstations with eventual deployment on on-board processors. ART-Ada technology enables this migration while Lisp-based technologies make it difficult if not impossible. In addition to reactive and cognitive agents, a meta-level agent would be needed to coordinate multiple agents and to provide meta-level control
A knowledge representation meta-model for rule-based modelling of signalling networks
The study of cellular signalling pathways and their deregulation in disease
states, such as cancer, is a large and extremely complex task. Indeed, these
systems involve many parts and processes but are studied piecewise and their
literatures and data are consequently fragmented, distributed and sometimes--at
least apparently--inconsistent. This makes it extremely difficult to build
significant explanatory models with the result that effects in these systems
that are brought about by many interacting factors are poorly understood.
The rule-based approach to modelling has shown some promise for the
representation of the highly combinatorial systems typically found in
signalling where many of the proteins are composed of multiple binding domains,
capable of simultaneous interactions, and/or peptide motifs controlled by
post-translational modifications. However, the rule-based approach requires
highly detailed information about the precise conditions for each and every
interaction which is rarely available from any one single source. Rather, these
conditions must be painstakingly inferred and curated, by hand, from
information contained in many papers--each of which contains only part of the
story.
In this paper, we introduce a graph-based meta-model, attuned to the
representation of cellular signalling networks, which aims to ease this massive
cognitive burden on the rule-based curation process. This meta-model is a
generalization of that used by Kappa and BNGL which allows for the flexible
representation of knowledge at various levels of granularity. In particular, it
allows us to deal with information which has either too little, or too much,
detail with respect to the strict rule-based meta-model. Our approach provides
a basis for the gradual aggregation of fragmented biological knowledge
extracted from the literature into an instance of the meta-model from which we
can define an automated translation into executable Kappa programs.Comment: In Proceedings DCM 2015, arXiv:1603.0053
Definition and Complexity of Some Basic Metareasoning Problems
In most real-world settings, due to limited time or other resources, an agent
cannot perform all potentially useful deliberation and information gathering
actions. This leads to the metareasoning problem of selecting such actions.
Decision-theoretic methods for metareasoning have been studied in AI, but there
are few theoretical results on the complexity of metareasoning.
We derive hardness results for three settings which most real metareasoning
systems would have to encompass as special cases. In the first, the agent has
to decide how to allocate its deliberation time across anytime algorithms
running on different problem instances. We show this to be
-complete. In the second, the agent has to (dynamically) allocate
its deliberation or information gathering resources across multiple actions
that it has to choose among. We show this to be -hard even when
evaluating each individual action is extremely simple. In the third, the agent
has to (dynamically) choose a limited number of deliberation or information
gathering actions to disambiguate the state of the world. We show that this is
-hard under a natural restriction, and -hard in
general
Epistemic virtues, metavirtues, and computational complexity
I argue that considerations about computational complexity show that all finite agents need characteristics like those that have been called epistemic virtues. The necessity of these virtues follows in part from the nonexistence of shortcuts, or efficient ways of finding shortcuts, to cognitively expensive routines. It follows that agents must possess the capacities – metavirtues –of developing in advance the cognitive virtues they will need when time and memory are at a premium
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