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— In this paper, we consider the general multiple-target tracking problem in which an unknown number of targets appears and disappears at random times and the goal is to find the tracks of targets from noisy observations. We propose an efficient real-time algorithm that solves the data association problem and is capable of initiating and terminating a(More)
The long-term goal of AI is the creation and understanding of intelligence. This requires a notion of intelligence that is precise enough to allow the cumulative development of robust systems and general results. The concept of rational agency has long been considered a leading candidate to fulfill this role. This paper, which updates a much earlier version(More)
Memory-bounded algorithms such as Korf's IDA* and Chakrabarti et al's MA* are designed to overcome the impractical memory requirements of heuristic search algorithms such as A*. It is shown that IDA* is ineecient when the heuristic function can take on a large number of values; this is a consequence of using too little memory. Two new algorithms are(More)
Bayesian networks are graphical representations of probability distributions. Over the last decade, these representations have become the method of choice for representation of uncertainly in artiicial intelligence. Today, they play a crucial role in modern expert systems, diagnosis engines, and decision support systems. In recent years, there has been much(More)
t is hard to escape the nagging suspicion that creating machines smarter than ourselves might be a problem. After all, if gorillas had accidentally created humans way back when, the now endangered primates probably would be wishing they had not done so. But why, specifically , is advanced artificial intelligence a problem? Hollywood's theory that(More)
• Standard search problem: – state is a "black box " – any data structure that supports successor function, heuristic function, and goal test • CSP: – state is defined by variables X i with values from domain D i – goal test is a set of constraints specifying allowable combinations of values for subsets of variables • Simple example of a formal(More)
This paper presents Markov chain Monte Carlo data association (MCMCDA) for solving data association problems arising in multiple-target tracking in a cluttered environment. When the number of targets is fixed, the single-scan version of MCMCDA approximates joint probabilistic data association (JPDA). Although the exact computation of association(More)