Download Decision Making Under Uncertainty: Theory and Application by Mykel J. Kochenderfer et al. PDF

By Mykel J. Kochenderfer et al.

Many vital difficulties contain selection making lower than uncertainty -- that's, deciding on activities in response to frequently imperfect observations, with unknown results. Designers of computerized determination aid platforms needs to keep in mind a number of the resources of uncertainty whereas balancing the a number of ambitions of the procedure. This e-book presents an creation to the demanding situations of selection making less than uncertainty from a computational standpoint. It provides either the speculation in the back of selection making types and algorithms and a set of instance purposes that variety from speech reputation to airplane collision avoidance.

Focusing on tools for designing selection brokers, making plans and reinforcement studying, the booklet covers probabilistic types, introducing Bayesian networks as a graphical version that captures probabilistic relationships among variables; application idea as a framework for figuring out optimum determination making lower than uncertainty; Markov choice methods as a mode for modeling sequential difficulties; version uncertainty; nation uncertainty; and cooperative determination making related to a number of interacting brokers. a sequence of functions exhibits how the theoretical techniques might be utilized to structures for attribute-based individual seek, speech functions, collision avoidance, and unmanned airplane chronic surveillance.

Decision Making below Uncertainty unifies learn from diverse groups utilizing constant notation, and is on the market to scholars and researchers throughout engineering disciplines who've a few earlier publicity to likelihood idea and calculus. it may be used as a textual content for complicated undergraduate and graduate scholars in fields together with desktop technological know-how, aerospace and electric engineering, and administration technology. it is going to even be a priceless specialist reference for researchers in a number of disciplines.

Show description

Read Online or Download Decision Making Under Uncertainty: Theory and Application PDF

Similar intelligence & semantics books

The Artificial Life Route To Artificial Intelligence: Building Embodied, Situated Agents

This quantity is the direct results of a convention within which a couple of prime researchers from the fields of synthetic intelligence and biology accumulated to envision no matter if there has been any flooring to imagine new AI paradigm was once forming itself and what the fundamental components of this new paradigm have been.

An Introduction to Computational Learning Theory

Emphasizing problems with computational potency, Michael Kearns and Umesh Vazirani introduce a few important issues in computational studying thought for researchers and scholars in synthetic intelligence, neural networks, theoretical laptop technology, and statistics. Computational studying concept is a brand new and swiftly increasing zone of study that examines formal types of induction with the pursuits of studying the typical equipment underlying effective studying algorithms and picking out the computational impediments to studying.

Ontology-Based Multi-Agent Systems

The Semantic internet has given loads of impetus to the improvement of ontologies and multi-agent structures. a number of books have seemed which debate the improvement of ontologies or of multi-agent structures individually on their lonesome. The turning out to be interplay among agnets and ontologies has highlighted the necessity for built-in improvement of those.

Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches

The tough and fuzzy set techniques provided right here open up many new frontiers for endured learn and improvement. Computational Intelligence and have choice presents readers with the heritage and primary principles at the back of function choice (FS), with an emphasis on ideas in accordance with tough and fuzzy units.

Additional resources for Decision Making Under Uncertainty: Theory and Application

Sample text

7. S. Boyd and L. Vandenberghe, Convex Optimization. New York: Cambridge University Press, 2004. 8. D. N. Tsitsiklis, Introduction to Linear Optimization. Belmont, MA: Athena Scientific, 1997. 9. P. H. Żak, An Introduction to Optimization, 4th ed. Hoboken, NJ: Wiley, 2013. 10. M. Ghallab, D. Nau, and P. Traverso, Automated Planning: Theory and Practice. San Francisco: Morgan Kaufmann, 2004. 11. M. LaValle, Planning Algorithms. New York: Cambridge University Press, 2006. 4. PEFMT Mykel J. Kochenderfer Rational decision making requires reasoning about one’s uncertainty and objectives.

Because we want larger wingspans to result in larger cross sections, we should be sure to make θ1 positive. PEFMT wingspans will also have infinitesimally small cross sections, and so θ2 should probably be 0. The parameter θ3 controls the amount of variance in the linear relationship between c and w. In reality, C depends on both W and M . We can simply make the parameters used in the linear Gaussian distribution dependent on M : P (c | w, m) = (c | θ1 w + θ2 , θ3 ) (c | θ4 w + θ5 , θ6 ) if m 0 .

For compactness throughout this book, we will use colon notation occasionally in subscripts. For example, O1:n is a compact way to write O1 , . . , On . The naive Bayes model is called naive because it assumes conditional independence between the evidence variables given the class. 5, we can say (Oi ⊥O j | C ) for all i = j . Of course, if these conditional independence assumptions do not hold, then we can add the necessary directed edges between the observed features.  *OGFSFODF C O1  $MBTT ··· On 0CTFSWFE GFBUVSFT 'JHVSF  *OGFSFODF GPS DMBTTJýDBUJPO VTJOH B OBJWF #BZFT NPEFM C Oi $MBTT 0CTFSWFE GFBUVSFT i =1:n 'JHVSF  1MBUF SFQSFTFOUBUJPO PG B OBJWF #BZFT NPEFM In the naive Bayes model, we have to specify the prior P (C ) and the class-conditional distribution P (Oi | C ).

Download PDF sample

Rated 4.01 of 5 – based on 44 votes