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A central challenge in building advanced sensor networks will be the development of distributed and robust control for such networks that scales to thousands of intelligent sensors [8]. Appropriately structuring where and when control and interpretation activities are done is key to the effective operation of the network. This structuring must be adaptive to changing network conditions such as new...
For many years AI researchers have sought to understand the nature of intelligence primarily by creating artificially intelligent computer systems. Studies of human intelligence have had less influence on AI, partly because of the great difficulty in directly observing human brain activity. In recent years, new methods for observing brain activity have become available, notably functional Magnetic...
Predicting protein structure is a fundamental problem in biology, especially in the genomic era where over one third of newly discovered genes have unknown structure and function. Because sequence and structure data (hence training sets) continue to grow exponentially, this area is ideally suited for machine learning approaches. Neural networks, in particular, have had remarkable success and have...
Within the law, the traditional test for attributing causal responsibility is the “but-for”. test, which asks whether, ‘but for’ the defendant’s wrongful act, the injury complained of would have occurred. This definition conforms to common intuitions regarding causation, but gives non-intuitive results in complex situations of overdetermination where two or more potential causes are present. To handle...
In this paper, we describe our experimentations in evaluating answer formulation for question-answering (QA) systems. In the context of QA, answer formulation can serve two purposes: improving answer extraction or improving human-computer interaction (HCI). Each purpose has di.erent precision/recall requirements. We present our experiments for both purposes and argue that formulations of better grammatical...
Creating realistic artificially-intelligent characters is seen as one of the major challenges of the commercial games industry. Historically, character behavior has been specified using simple finite state machines and, more recently, by AI scripting languages. These languages are relatively “simple”, in part because the language has to serve three user communities: game designers, game programmers,...
Agent communication languages (ACLs) invoke speech act theory and define individual message types by reference to particular combinations of beliefs and desires of the speaker (feasibility preconditions). Even when the mental states are restricted to a small set of nested beliefs, it seems that there might be a very large number of different possible preconditions, and therefore a very large number...
Memoization is a well-known method which makes use of a table of previously-computed results in order to ensure that parts of a search (or computation)s pace are not revisited. A new technique is presented which enables the systematic and selective memoization of a wide range of algorithms. The technique overcomes disadvantages of previous approaches. In particular, the proposed technique can help...
The search for all solutions in the crypto-arithmetic problem is performed with two kinds of adaptive parallel genetic algorithm. Since the performance of genetic algorithms is critically determined by the architecture and parameters involved in the evolution process, an adaptive control is implemented on two parameters governing the relative percentages of preserved (survived) individuals and reproduced...
The multi-processor total tardiness problem (MPTTP) is an NP-hard scheduling problem, in which the the goal is to minimise the tardness of a set of jobs that are processed on a number of processors. Exact algorithms like branch and bound have proven to be impractical for the MPTTP, leaving stochastic local search (SLS) algorithms as the main alternative to find high-quality schedules. Among the available...
Many AI tasks can be formalized as constraint satisfaction problems (CSPs), which involve finding values for variables subject to constraints. While solving a CSP is an NP-complete task in general, tractable classes of CSPs have been identified based on the structure of the underlying constraint graphs. Much effort has been spent on exploiting structural properties of the constraint graph to improve...
MAX-SAT, the optimisation variant of the satisfiability problem in propositional logic, is an important and widely studied combinatorial optimisation problem with applications in AI and other areas of computing science. In this paper, we present a new stochastic local search (SLS) algorithm for MAX-SAT that combines Iterated Local Search and Tabu Search, two well-known SLS methods that have been successfully...
In this paper, we study the behaviour of the Scaling and Probabilistic Smoothing (SAPS) dynamic local search algorithm on the unweighted MAX-SAT problem. MAX-SAT is a conceptually simple combinatorial problem of substantial theoretical and practical interest; many application-relevant problems, including scheduling problems or most probable explanation finding in Bayes nets, can be encoded and solved...
This paper reviews the main approaches for extending arc consistency propagation in constraint optimization frameworks and discusses full and partial arc consistency propagation based on Larrosa’s W-NC* and W-AC*2001 algorithms [Larrosa 2002]. We implement these full/partial propagation algorithms in branch and bound search and compare their performance on MaxCSP models. We empirically demonstrate...
We describe TimeSleuth, a hybrid tool based on the C4.5 classification software, which is intended for the discovery of temporal/causal rules. Temporally ordered data are gathered from observable attributes of a system, and used to discover relations among the attributes. In general, such rules could be atemporal or temporal. We evaluate TimeSleuth using synthetic data sets with well-known causal...
The selective transfer of task knowledge within the context of artificial neural networks is studied using a modified version of ηMTL (multiple task learning) previously reported. sMTL is a knowledge based inductive learning system that uses prior task knowledge and stochastic noise to adjust its inductive bias when learning a new task. The MTL representation of previously learned and consolidated...
Discovering association rules is one of the important tasks in data mining. While most of the existing algorithms are developed for efficient mining of frequent patterns, it has been noted recently that some of the infrequent patterns, such as indirect associations, provide useful insight into the data. In this paper, we propose an efficient algorithm, called HI-mine, based on a new data structure,...
The problem of repair and maintenance of complex systems, such as aircraft, cars and trucks is a nontrivial task. Maintenance technicians must use a great amount of knowledge and information resources to solve problems that may occur. This paper describes a semi-automated tool that sorts through the mass of information that a maintenance technician must consult in order to make a repair, thus helping...
We present a statistical method using n-gram language models to identify session boundaries in a large collection of Livelink log data. The identified sessions are then used for association rule learning. Unlike the traditional ad hoc timeout method, which uses fixed time thresholds for session identification, our method uses an information theoretic approach that provides a natural technique for...
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