Formation of adjective, noun and verb concepts through affordances

Turan, Ayşe
Within the air defense domain, the Weapon-Target Allocation problem is a fundamental problem. This problem deals with the allocation of a set ofiring units or weapons to a set of hostile targets so that the total expected effect on targets is maximized. The Weapon-Target Allocation problem has been proven to be NP-Complete by Lloyd and Witsenhausen [14]. In this thesis, the use of various algorithms including search algorithms, maximum marginal return algorithms, evolutionary algorithms and bipartite graph matching algorithms are demonstrated to solve the problem. Algorithms from the literature are adjusted to the problem and implemented. In addition, existing algorithms are improved by taking care of the maximum allowed time criterion. A testbed is developed to be able to compare the algorithms. The developed testbed allows users to implement new algorithms and compare the algorithms that are selected by the users easily. Using the testbed, implemented algorithms are compared based on optimality and performance criteria. The results are examined and by combining the algorithms that give better results, a new algorithm is proposed to solve the problem more effciently. The proposed algorithm is also compared to the other algorithms and computational results of the algorithms are presented


A context aware model for autonomous agent stochastic planning
Ekmekci, Ömer; Polat, Faruk (Elsevier BV, 2019-02-01)
Markov Decision Processes (MDPs) are not able to make use of domain information effectively due to their representational limitations. The lacking of elements which enable the models be aware of context, leads to unstructured representation of that problem such as raw probability matrices or lists. This causes these tools significantly less efficient at determining a useful policy as the state space of a task grows, which is the case for more realistic problems having localized dependencies between states a...
Utilizing business process models for requirements elicitation
Demirörs, Onur; Gencel, C; Tarhan, A (2003-09-06)
Acquisition of software intensive systems demands significant work on requirements prior to establishing the contract. The acquirer needs to understand the domain, needs, and constraints of the project clearly in order to make realistic size and effort estimates, and to have a solid foundation for defining contract requirements. In this study, an approach for requirements elicitation based on business processes is investigated. The approach proposes determination of requirements of a software intensive syst...
Analysis of Frequency Domain Oversampled MMSE SC-FDE
Balevi, Eren; Yılmaz, Ali Özgür (2016-02-01)
Frequency domain oversampling (FDO) is investigated to improve the error performance of MMSE SC-FDE for multipath channels. The impact of FDO is analyzed in regard to the outage probability. The results show that FDO can significantly enhance the performance of MMSE SC-FDE when the ratio of block length to channel memory length is small such as in underwater acoustic channels. Its advantage is observed for moderate block length to channel memory length ratio for larger constellation sizes.
A control system using behaviour hierarchies and neuro-fuzzy approach
Arslan, Dilek; Alpaslan, Ferda Nur; Department of Computer Engineering (2005)
In agent based systems, especially in autonomous mobile robots, modelling the environment and its changes is a source of problems. It is not always possible to effectively model the uncertainity and the dynamic changes in complex, real-world domains. Control systems must be robust to changes and must be able to handle these uncertainties to overcome this problem. In this study, a reactive behaviour based agent control system is modelled and implemented. The control system is tested in a navigation task usin...
Learning cooperation in hunter-prey problem via state abstraction
İşçen, Atıl; Polat, Faruk; Department of Computer Engineering (2009)
Hunter-Prey or Prey-Pursuit problem is a common toy domain for Reinforcement Learning, but the size of the state space is exponential in the parameters such as size of the grid or number of agents. As the size of the state space makes the flat Q-learning impossible to use for different scenarios, this thesis presents an approach to make the size of the state space constant by producing agents that use previously learned knowledge to perform on bigger scenarios containing more agents. Inspired from HRL metho...
Citation Formats
A. Turan, “Formation of adjective, noun and verb concepts through affordances,” M.S. - Master of Science, Middle East Technical University, 2012.