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The following topics are dealt with: information retrieval; intelligent agent; intelligent tutoring system; e-learning system; knowledge discovery; knowledge extraction; knowledge representation; reasoning; machine learning; neural network; natural language processing; speech processing; vision and video processing.
In this paper, we present a new hypergraph partitioning algorithm that jointly optimizes the number of hyperedge cuts and the number of shared vertices in nonlinear constrained optimization problems. By exploiting the localities of constraints with respect to their variables, we propose to partition the constraints into subproblems. We use a relaxed global search to solve the subproblems and resolve...
Semi-markov decision processes (SMDP) are continuous time generalizations of discrete time Markov Decision Process. A number of value and policy iteration algorithms have been developed for the solution of SMDP problem. But solving SMDP problem requires prior knowledge of the deterministic kernels, and suffers from the curse of dimensionality. In this paper, we present the steepest descent direction...
This paper proposes a novel methodology to solve the graph coloring problem (GCP) using the Q'tron neural- network (NN) model. The Q'tron NN for GCP will be built as a known-energy system. This can make the NN local- minima-free and perform the so-called goal-directed search. Consider k-GCP as a goal to solve a GCP using at most k different colors. By continuously refining our goal, i.e., decreasing...
Influence diagrams (ID) are a graphical computational model developed for decision making with uncertainty, based on probability inference. The unconstrained version of this model (UID) drops the restriction of linear ordering of decisions. It adds expressiveness to the model, but it brings an exponential growth of complexity of the already computationally intensive algorithm for optimal ID evaluation...
Practical optimization problems often have objective functions that cannot be easily calculated. As a result, comparison-based algorithms that solve such problems use comparison functions that are imperfect (i.e. they may make errors). Machine learning algorithms that search for game-playing programs are typically imperfect comparison algorithms. This paper presents M2ICAL, an algorithm analysis tool...
In this paper, a two-dimensional Cellular Automaton (CA) model simulates the evacuation process of a crowd responding to fire spread. The crowd consists of individuals and its behaviour is modelled by the response of each individual to a rule that directs him/her to the nearest exit. Furthermore, fire spreading and movements of the crowd members while approaching the fire are successfully simulated...
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