Computational Intelligence in Intrusion Detection System
... One of the important research challenges for constructing high performance NIDS is dealing with data containing large number of features. Extraneous features can make it harder to detect suspicious behavior patterns, causing slow training and testing process, higher resource consumption as well as p ...
... One of the important research challenges for constructing high performance NIDS is dealing with data containing large number of features. Extraneous features can make it harder to detect suspicious behavior patterns, causing slow training and testing process, higher resource consumption as well as p ...
Generalised Integer Programming Based on Logically Defined
... of just being linear inequalities). The set of allowed relations is defined using a many-valued logic and the resulting class of relations have provably strong modelling properties. We give sufficient conditions for when such problems are polynomial-time solvable and we prove that they are APX-hard ...
... of just being linear inequalities). The set of allowed relations is defined using a many-valued logic and the resulting class of relations have provably strong modelling properties. We give sufficient conditions for when such problems are polynomial-time solvable and we prove that they are APX-hard ...
Planning Algorithms for Classical Planning Planning Hierarchy of
... This is breadth-first search / iterative deepening. Guarantees shortest horizon length, but is slow. ...
... This is breadth-first search / iterative deepening. Guarantees shortest horizon length, but is slow. ...
Introduction to AI - Florida Tech Department of Computer Sciences
... What can you do with this course? • Some companies prefer students with AI background • Current boom in Data Science (a new name for Data Mining) • Helps in other advanced courses ...
... What can you do with this course? • Some companies prefer students with AI background • Current boom in Data Science (a new name for Data Mining) • Helps in other advanced courses ...
Commonsense Reasoning by Integrating Simulation and Logic
... Of course, with (typically) only a forward mode of reasoning and many critical limitations, simulations are not immediately useful as a mechanism for commonsense reasoning. However, in combination with a suitable automatic reasoning system for an expressive logic, these limitations can be avoided. P ...
... Of course, with (typically) only a forward mode of reasoning and many critical limitations, simulations are not immediately useful as a mechanism for commonsense reasoning. However, in combination with a suitable automatic reasoning system for an expressive logic, these limitations can be avoided. P ...
Contest File (2016)
... To receive full credit, the presentation must be legible, orderly, clear, and concise. If a problem says “list” or “compute,” you need not justify your answer. If a problem says “determine,” “find,” or “show,” then you must show your work or explain your reasoning to receive full credit, although su ...
... To receive full credit, the presentation must be legible, orderly, clear, and concise. If a problem says “list” or “compute,” you need not justify your answer. If a problem says “determine,” “find,” or “show,” then you must show your work or explain your reasoning to receive full credit, although su ...
Using a Goal-Agenda and Committed Actions in Real
... Three main extensions of this preliminary work were proposed. The first one, called landmarks planning, was introduced by [11]. It does not only order the (top-level) goals, but also the sub-goals that will necessarily arise during planning, i.e., it also takes into account what they called the “lan ...
... Three main extensions of this preliminary work were proposed. The first one, called landmarks planning, was introduced by [11]. It does not only order the (top-level) goals, but also the sub-goals that will necessarily arise during planning, i.e., it also takes into account what they called the “lan ...
HAPLO-ASP: Haplotype Inference Using Answer Set Programming
... by H APLO -ASP, and its applicability and effectiveness on real data in comparison with the other existing approaches. ...
... by H APLO -ASP, and its applicability and effectiveness on real data in comparison with the other existing approaches. ...
Heuristics, Planning and Cognition
... heuristics that take the form of evaluation functions and which provide quick but approximate estimates of the distance or cost-to-go from a given state to the goal. These heuristic evaluation functions provide the search with a sense of direction with actions resulting in states that are closer to ...
... heuristics that take the form of evaluation functions and which provide quick but approximate estimates of the distance or cost-to-go from a given state to the goal. These heuristic evaluation functions provide the search with a sense of direction with actions resulting in states that are closer to ...
Real-Time Search for Autonomous Agents and
... Since the second smallest values are always maintained, RTA* can make locally optimal decisions. Theorem 4 In a tree problem space, each mo¨ e made by RTA* is along a path whose estimated cost toward the goal is minimum based on the already-obtained information. However, this result cannot be extend ...
... Since the second smallest values are always maintained, RTA* can make locally optimal decisions. Theorem 4 In a tree problem space, each mo¨ e made by RTA* is along a path whose estimated cost toward the goal is minimum based on the already-obtained information. However, this result cannot be extend ...
Learning Humanoid Reaching Tasks in Dynamic Environments
... Line 1 requires selection of an example task from the procedure will detect extra attractors in difficult regions database that has the largest similarity to the current task. around obstacles that always cause collisions. If we apply the The similarity value is computed by our task comparison seque ...
... Line 1 requires selection of an example task from the procedure will detect extra attractors in difficult regions database that has the largest similarity to the current task. around obstacles that always cause collisions. If we apply the The similarity value is computed by our task comparison seque ...
Genetic algorithm
In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.