55,001 research outputs found
META-INTERPRETERS FOR RULE-BASED REASONING UNDER UNCERTAINTY
One of the key challenges in designing expert systems is a credible representation
of uncertainty and partial belief. During the past decade, a number of
rule-based belief languages were proposed and implemented in applied systems.
Due to their quasi-probabilistic nature, the external validity of these
languages is an open question. This paper discusses the theory of belief revision
in expert systems through a canonical belief calculus model which is
invariant across different languages. A meta-interpreter for non-categorical
reasoning is then presented. The purposes of this logic model is twofold:
first, it provides a clear and concise conceptualization of belief representation
and propagation in rule-based systems. Second, it serves as a working
shell which can be instantiated with different belief calculi. This enables
experiments to investigate the net impact of alternative belief languages on
the external validity of a fixed expert system.Information Systems Working Papers Serie
Beliefs and Conflicts in a Real World Multiagent System
In a real world multiagent system, where the
agents are faced with partial, incomplete and
intrinsically dynamic knowledge, conflicts are
inevitable. Frequently, different agents have
goals or beliefs that cannot hold simultaneously.
Conflict resolution methodologies have to be
adopted to overcome such undesirable occurrences.
In this paper we investigate the application of
distributed belief revision techniques as the support
for conflict resolution in the analysis of the
validity of the candidate beams to be produced
in the CERN particle accelerators.
This CERN multiagent system contains a higher
hierarchy agent, the Specialist agent, which
makes use of meta-knowledge (on how the conflicting
beliefs have been produced by the other
agents) in order to detect which beliefs should be
abandoned. Upon solving a conflict, the Specialist
instructs the involved agents to revise their
beliefs accordingly.
Conflicts in the problem domain are mapped into
conflicting beliefs of the distributed belief revision
system, where they can be handled by
proven formal methods. This technique builds
on well established concepts and combines them
in a new way to solve important problems. We
find this approach generally applicable in several
domains
Logic, self-awareness and self-improvement: The metacognitive loop and the problem of brittleness
This essay describes a general approach to building perturbation-tolerant autonomous systems, based on the conviction that artificial agents should be able notice when something is amiss, assess the anomaly, and guide a solution into place. We call this basic strategy of self-guided learning the metacognitive loop; it involves the system monitoring, reasoning about, and, when necessary, altering its own decision-making components. In this essay, we (a) argue that equipping agents with a metacognitive loop can help to overcome the brittleness problem, (b) detail the metacognitive loop and its relation to our ongoing work on time-sensitive commonsense reasoning, (c) describe specific, implemented systems whose perturbation tolerance was improved by adding a metacognitive loop, and (d) outline both short-term and long-term research agendas
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