291,778 research outputs found
Concept of A.I. Based Knowledge Generator
An important feature of the currently used artificial intelligence systems is their anthropomorphism. The tool of inductive empirical systems is a neural network that simulates the human brain and operates in the "black box" mode. Deductive analytical systems for representation of knowledge use transparent formalized models and algorithms, for example, algorithms of logical inference. They solve many intellectual problems, the solution of which can do without a "deep" anthropomorphic AI. On the other hand, the solution of these problems leads to the formation of alternative artificial intelligence systems. We propose the formation of artificial intelligence systems based on the following principles: exclusion of black box technologies; domination of data conversion systems: the use of direct mathematical modeling. The base of the system is a simulator - a module that simulates a given object. The ontological module selectively extracts structured sets of functional links from the simulator and fills them with corresponding data sets. The final (custom) representation of knowledge is carried out with the help of special interfaces. The concept of simulation-ontological artificial intelligence, based on the principles outlined above, is implemented in the form of parametric analysis in the configuration space and forms the methodological basis of the AI-platform for e-learning
Experimental evaluation of algorithms forsolving problems with combinatorial explosion
Solving problems with combinatorial explosionplays an important role in decision-making, sincefeasible or optimal decisions often depend on anon-trivial combination of various factors. Gener-ally, an effective strategy for solving such problemsis merging different viewpoints adopted in differ-ent communities that try to solve similar prob-lems; such that algorithms developed in one re-search area are applicable to other problems, orcan be hybridised with techniques in other ar-eas. This is one of the aims of the RCRA (Ra-gionamento Automatico e Rappresentazione dellaConoscenza) group,1the interest group of the Ital-ian Association for Artificial Intelligence (AI*IA)on knowledge representation and automated rea-soning, which organises its annual meetings since1994
Planning in POMDPs Using Multiplicity Automata
Planning and learning in Partially Observable MDPs (POMDPs) are among the
most challenging tasks in both the AI and Operation Research communities.
Although solutions to these problems are intractable in general, there might be
special cases, such as structured POMDPs, which can be solved efficiently. A
natural and possibly efficient way to represent a POMDP is through the
predictive state representation (PSR) - a representation which recently has
been receiving increasing attention. In this work, we relate POMDPs to
multiplicity automata- showing that POMDPs can be represented by multiplicity
automata with no increase in the representation size. Furthermore, we show that
the size of the multiplicity automaton is equal to the rank of the predictive
state representation. Therefore, we relate both the predictive state
representation and POMDPs to the well-founded multiplicity automata literature.
Based on the multiplicity automata representation, we provide a planning
algorithm which is exponential only in the multiplicity automata rank rather
than the number of states of the POMDP. As a result, whenever the predictive
state representation is logarithmic in the standard POMDP representation, our
planning algorithm is efficient.Comment: Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005
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