Data science and Ebola

Inaugural Lecture by Prof.dr. Aske Plaat on the acceptance of the position of professor of Data Science at the Universiteit Leiden on Monday 13 April 2015Algorithms and the Foundations of Software technolog

A theory of game trees, based on solution trees

In this paper a complete theory of game tree algorithms is presented, entirely based upon the notion of a solution tree. Two types of solution trees are distinguished: max and min solution trees respectively. We show that most game tree algorithms construct a superposition of a max and a min solution tree. Moreover, we formulate a general cut-off criterion in terms of solution trees. In the second half of this paper four well known algorithms, viz., alphabeta, SSS*, MTD and Scout are studied extensively. We show how solution trees feature in these algorithms and how the cut-off criterion is applied

Solution trees as a basis for game tree search

A game tree algorithm is an algorithm computing the minimax value of the root of a game tree. Many algorithms use the notion of establishing proofs that this value lies above or below some boundary value. We show that this amounts to the construction of a solution tree. We discuss the role of solution trees and critical trees in the following algorithms: Principal Variation Search, alpha-beta, and SSS-2. A general procedure for the construction of a solution tree, based on alpha-beta and Null-Window-Search, is given. Furthermore two new examples of solution tree-based algorithms are presented, that surpass alpha-beta, i.e., never visit more nodes than alpha-beta, and often less

Centralized Versus Decentralized Team Coordination Using Dynamic Scripting

Computer generated forces (CGFs) must display realistic behavior for tactical training simulations to yield an effective training experience. Tradionally, the behavior of CGFs is scripted. However, there are three drawbacks, viz. (1) scripting limits the adaptive behavior of CGFs, (2) creating scripts is difficult and (3) it requires scarce domain expertise. A promising machine learning technique is the dynamic scripting of CGF behavior. In simulating air combat scenarios, team behavior is important, both with and without communication. While dynamic scripting has been reported to be effective in creating behavior for single fighters, it has not often been used for team coordination. The dynamic scripting technique is sufficiently flexible to be used for different team coordination methods. In this paper, we report the first results on centralized coordination of dynamically scripted air combat teams, and compare these results to a decentralized approach from earlier work. We find that using the centralized approach leads to higher performance and more efficient learning, although creativity of the solutions seems bounded by the reduced complexity

A new paradigm for minimax search

This paper introduces a new paradigm for minimax game-tree search algorithms. MT is a memory-enhanced version of Pearl's Test procedure. By changing the way MT is called, a number of best-first game-tree search algorithms can be simply and elegantly constructed (including SSS*). Most of the assessments of minimax search algorithms have been based on simulations. However, these simulations generally do not address two of the key ingredients of high performance game-playing programs: iterative deepening and memory usage. This paper presents experimental data from three game-playing programs (checkers, Othello and chess), covering the range from low to high branching factor. The improved move ordering due to iterative deepening and memory usage results in significantly different results from those portrayed in the literature. Whereas some simulations show alpha-beta expanding almost 100% more leaf nodes than other algorithms [Marsland, Reinefeld & Schaeffer, 1987], our results showed variations of less than 20%. One new instance of our framework MTD(f) out-performs our best alpha-beta searcher (aspiration NegaScout) on leaf nodes, total nodes and execution time. To our knowledge, these are the first reported results that compare both depth-first and best-first algorithms given the same amount of memory

Onset of hypothyroidism after total laryngectomy:Effects of thyroid gland surgery and preoperative and postoperative radiotherapy

Background: To determine time of onset and risk of hypothyroidism after total laryngectomy (TL) with and without (hemi)thyroidectomy in relation to treatment regimen, that is, preoperative radiotherapy (RT-TL), postoperative radiotherapy (TL-RT), and postoperative re-irradiation (RT-TL-RT). Methods: Retrospective review of 128 patients treated by RT-TL (51 patients), TL-RT (55 patients), and RT-TL-RT (22 patients). Risk of hypothyroidism was determined by multivariable Cox regression analysis and euthyroid survival was calculated using Kaplan-Meier method. Results: Hypothyroidism developed in 69 (54%) patients. The median onset of hypothyroidism was later (P <.01) and the risk of hypothyroidism was lower (hazard ratio 0.49; P =.014) in the TL-RT group compared to both other treatment regimens. Euthyroid survival did not differ between the treatment regimens. Two years euthyroid survival was 24% with and 61% without (hemi)thyroidectomy (P <.001). Conclusions: Patients treated with TL-RT have later onset of hypothyroidism. Higher risk for hypothyroidism is associated with salvage TL after radiotherapy and (hemi)thyroidectomy