Physical Sciences and Mathematics Commons^™

Could A Robot Be District Attorney?, Stephen E. Henderson

Stephen E Henderson

No abstract provided.

Artificial Intelligence And Role-Reversible Judgment, Stephen E. Henderson, Kiel Brennan-Marquez

Stephen E Henderson

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

Martin Masek

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

Martin Masek

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.

Slaves To Big Data. Or Are We?, Mireille Hildebrandt

Mireille Hildebrandt

In this contribution the notion of Big Data is discussed in relation to the monetisation of personal data. The claim of some proponents as well as adversaries, that Big Data implies that ‘n = all’, meaning that we no longer need to rely on samples because we have all the data, is scrutinized and found both overly optimistic and unnecessarily pessimistic. A set of epistemological and ethical issues is presented, focusing on the implications of Big Data for our perception, cognition, fairness, privacy and due process. The article then looks into the idea of user centric personal data management, to …

Computational Intelligence And Decision Making: A Multidisciplinary Review, Renato Martins Alas, Sukanto Bhattacharya, Kuldeep Kumar

Kuldeep Kumar

The phenomenon of dynamic shift in our society called “speed up” has been part of the modern society since the middle of the eighteenth century. Its progressive development is already and will demand more speed in information processing. To cope with such fast pace demand of processing it is necessary to develop more sophisticated computational representation of the human brain. Computational Cognitive Neuroscience is the only realistic approach in reproducing the fundamental nature of human brain’s neurology. We support the biological computational representation of the human brain, based on fMRI imaging analysis, as more effective in the process of decision …

Analysis Of Uncertain Data: Evaluation Of Given Hypotheses, Anatole Gershman, Eugene Fink, Bin Fu, Jaime G. Carbonell

Jaime G. Carbonell

We consider the problem of heuristic evaluation of given hypotheses based on limited observations, in situations when available data are insufficient for rigorous statistical analysis.

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

Yuliya Lierler

Deriving Causal Explanation From Qualitative Model Reasoning, Rukaini Abdullah

Rukaini Abdullah

This paper discusses a qualitative simulator QRiOM that uses Qualitative Reasoning (QR) technique, and a process-based ontology to model, simulate and explain the behaviour of selected organic reactions. Learning organic reactions requires the application of domain knowledge at intuitive level, which is difficult to be programmed using traditional approach. The main objective of QRiOM is to help learners gain a better understanding of the fundamental organic reaction concepts, and to improve their conceptual comprehension on the subject by analyzing the multiple forms of explanation generated by the software. This paper focuses on the generation of explanation based on causal theories …

A Scientific Rationale For Belief In God?, Philip E. Graves

PHILIP E GRAVES

This paper presents a concise scientific rationale for the existence of God. The works of Ray Kurzweil and the many other artificial intelligence researchers provide a backdrop to the central thesis. An entity (computers or humans, it not mattering which) will eventually approach all-knowing. How much time passes before this occurs is not important. All-knowing is likely to be all-powerful insofar as knowledge leads to power, as has been our experience. One would suspect that this would be inclusive of time travel. The methods by which knowledge grows require “seed” facts to begin working. The seed facts can easily be, …

The Round Table Model: A Web-Oriented, Agent-Based Approach To Decision-Support Applications, Kym J. Pohl, Jens G. Pohl

Jens G. Pohl

Not unlike King Arthur relying on the infamous Round Table as the setting for consultation with his most trusted experts, agent-based, decision-support systems provide human decision makers with a means of solving complex problems through collaboration with collections of both human and computer-based expert agents. The Round Table Framework provides a formalized architecture together with a set of development and execution tools which can be utilized to design, develop, and execute agent-based, decision-support applications. Based on a three-tier architecture, Round Table incorporates forefront technologies including distributed-object servers, inference engines, and web-based presentation to provide a framework for collaborative, agent-based decision …

Learning From Labeled Features Using Generalized Expectation Criteria, Gregory Druck, Gideon Mann, Andrew Mccallum

Andrew McCallum

It is difficult to apply machine learning to new domains because often we lack labeled problem instances. In this paper, we provide a solution to this problem that leverages domain knowledge in the form of affinities between input features and classes. For example, in a baseball vs. hockey text classification problem, even without any labeled data, we know that the presence of the word puck is a strong indicator of hockey. We refer to this type of domain knowledge as a labeled feature. In this paper, we propose a method for training discriminative probabilistic models with labeled features and unlabeled …

Rapid Development Of Hindi Named Entity Recognition Using Conditional Random Fields And Feature Induction, Wei Li, Andrew Mccallum

Andrew McCallum

This paper describes our application of Conditional Random Fields (CRFs) with feature induction to a Hindi named entity recognition task. With only five days development time and little knowledge of this language, we automatically discover relevant features by providing a large array of lexical tests and using feature induction to automatically construct the features that most increase conditional likelihood. In an effort to reduce overfitting, we use a combination of a Gaussian prior and early-stopping based on the results of 10-fold cross validation.

Smoke And Mirrors Or Science? Teaching Law With Computers - A Reply To Cass Sunstein On Artificial Intelligence And Legal Science, Eric A. Engle

Eric A. Engle

The article explores the possibilities and limits of AI for teaching and modeling law.