Physical Sciences and Mathematics Commons^™

Math, Minds, Machines, Christopher V. Carlile

Chancellor’s Honors Program Projects

No abstract provided.

Generalizing Agent Plans And Behaviors With Automated Staged Observation In The Real-Time Strategy Game Starcraft, Zackary A. Gill

Theses and Dissertations - UTB/UTPA

In this thesis we investigate the processes involved in learning to play a game. It was inspired by two observations about how human players learn to play. First, learning the domain is intertwined with goal pursuit. Second, games are designed to ramp up in complexity, walking players through a gradual cycle of acquiring, refining, and generalizing knowledge about the domain. This approach does not rely on traces of expert play. We created an integrated planning, learning and execution system that uses StarCraft as its domain. The planning module creates command/event groupings based on the data received. Observations of unit behavior …

Multiview Semi-Supervised Learning With Consensus, Guangxia Li, Kuiyu Chang, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Obtaining high-quality and up-to-date labeled data can be difficult in many real-world machine learning applications. Semi-supervised learning aims to improve the performance of a classifier trained with limited number of labeled data by utilizing the unlabeled ones. This paper demonstrates a way to improve the transductive SVM, which is an existing semi-supervised learning algorithm, by employing a multiview learning paradigm. Multiview learning is based on the fact that for some problems, there may exist multiple perspectives, so called views, of each data sample. For example, in text classification, the typical view contains a large number of raw content features such …

Parsing Combinatory Categorial Grammar Via Planning In Answer Set Programming, Yuliya Lierler, Peter Schueller

Computer Science Faculty Books and Monographs

Essay, Parsing Combinatory Categorial Grammar via Planning in Answer Set Programming, from Correct reasoning: essays on logic-based AI in honour of Vladimir Lifschitz, co-authored by Yuliya Lierler, UNO faculty member. Combinatory categorial grammar (CCG) is a grammar formalism used for natural language parsing. CCG assigns structured lexical categories to words and uses a small set of combinatory rules to combine these categories to parse a sentence. In this work we propose and implement a new approach to CCG parsing that relies on a prominent knowledge representation formalism, answer set programming (ASP) - a declarative programming paradigm. We formulate the …

Correct Reasoning: Essays On Logic-Based Ai In Honour Of Vladimir Lifschitz, Esta Erdem, Joohyung Lee, Yuliya Lierler, David Pearce

Faculty Books and Monographs

Co-edited by Yuliya Lierler, UNO faculty member.

Essay, Parsing Combinatory Categorial Grammar via Planning in Answer Set Programming, co-authored by Yuliya Lierler, UNO faculty member.

This Festschrift published in honor of Vladimir Lifschitz on the occasion of his 65th birthday presents 39 articles by colleagues from all over the world with whom Vladimir Lifschitz had cooperation in various respects. The 39 contributions reflect the breadth and the depth of the work of Vladimir Lifschitz in logic programming, circumscription, default logic, action theory, causal reasoning and answer set programming.

Using Monte Carlo Tree Search For Replanning In A Multistage Simultaneous Game, Daniel Beard, Philip Hingston, Martin Masek

Research outputs 2012

In this study, we introduce MC-TSAR, a Monte Carlo Tree Search algorithm for strategy selection in simultaneous multistage games. We evaluate the algorithm using a battle planning scenario in which replanning is possible. We show that the algorithm can be used to select a strategy that approximates a Nash equilibrium strategy, taking into account the possibility of switching strategies part way through the execution of the scenario in the light of new information on the progress of the battle.

A Multimodal Problem For Competitive Coevolution, Philip Hingston, Tirtha Ranjeet, Chiou Peng Lam, Martin Masek

Research outputs 2012

Coevolutionary algorithms are a special kind of evolutionary algorithm with advantages in solving certain specific kinds of problems. In particular, competitive coevolutionary algorithms can be used to study problems in which two sides compete against each other and must choose a suitable strategy. Often these problems are multimodal - there is more than one strong strategy for each side. In this paper, we introduce a scalable multimodal test problem for competitive coevolution, and use it to investigate the effectiveness of some common coevolutionary algorithm enhancement techniques.

Comparing Ai Archetypes And Hybrids Using Blackjack, Robert Edward Noonan

All Graduate Theses, Dissertations, and Other Capstone Projects

The discipline of artificial intelligence (AI) is a diverse field, with a vast variety of philosophies and implementations to consider. This work attempts to compare several of these paradigms as well as their variations and hybrids, using the card game of blackjack as the field of competition. This is done with an automated blackjack emulator, written in Java, which accepts computer-controlled players of various AI philosophies and their variants, training them and finally pitting them against each other in a series of tournaments with customizable rule sets. In order to avoid bias towards any particular implementation, the system treats each …

Parsing Combinatory Categorial Grammar Via Planning In Answer Set Programming, Yuliya Lierler, Peter Schueller

Yuliya Lierler