Homeless youth are prone to human immunodeficiency virus (HIV) due to their engagement in high-risk behavior such as unprotected sex, sex under influence of drugs, and so on. Many nonprofit agencies conduct interventions to educate and train a select group of homeless youth about HIV prevention and treatment practices and rely on word-of-mouth spread of information through their one single social network Previous work in strategic selection of intervention participants does not handle uncertainties in the social networks' structure and evolving network state, potentially causing significant shortcomings in spread of information. Thus, we developed PSINET, a decision-support system to aid the agencies in this task. PSINET includes the following key novelties: (1) it handles uncertainties in network structure and evolving network state; (2) it addresses these uncertainties by using POMDPs in influence maximization; and (3) it provides algorithmic advances to allow high-quality approximate solutions for such POMDPs. Simulations show that PSINET achieves around 60 percent more information spread over the current state of the art.

A hybrid fuzzy knowledge-based system with crisp and fuzzy rules as well as numerical methods was developed for multiobjective optimization of power distribution system operation. The development process and knowledge-acquisition process for the fuzzy knowledge-based system are described in detail. Fuzzy sets are defined for recent temperature trend, line section loading, transformer aging, voltage-level guidelines, and the degree of desirability of a proposed switching combination. After a heuristic preprocessor proposes a list of switch openings that would seem to reduce system losses, network radiality rules consider whether to open a particular switch and find a corresponding switch that can be closed to maintain radiality. Network parameter rules determine whether the proposed switching combination will violate network integrity.

A Bayesian network is a compact, expressive representation of uncertain relationships among parameters in a domain. In this article, I introduce basic methods for computing with Bayesian networks, starting with the simple idea of summing the probabilities of events of interest. The article introduces major current methods for exact computation, briefly surveys approximation methods, and closes with a brief discussion of open issues. Often, truth is more elusive, and categorical statements can only be made by judgment of the likelihood or other ordinal attribute of competing propositions. Probability theory is the oldest and best-understood theory for representing and reasoning about such situations, but early AI experimental efforts at applying probability theory were disappointing and only confirmed a belief among AI researchers that those who worried about numbers were "missing the point."

I highlight several of these applications, using a simple energy storage problem as a case application. Using this setting, I describe a modeling framework that is based on five fundamental dimensions and that is more natural than the standard canonical form widely used in the reinforcement learning community. The framework focuses on finding the best policy, where I identify four fundamental classes of policies consisting of policy function approximations (PFAs), cost function approximations (CFAs), policies based on value function approximations (VFAs), and look-ahead policies. There is the familiar array of decisions: discrete actions, continuous controls, and vector-valued (and possibly integer) decisions. The tools for these problems are drawn from computer science, engineering, applied math, and operations research.

We provide a brief overview of recent direct preference elicitation methods: these methods ask users to answer (ideally, a small number of) queries regarding their preferences and use this information to recommend a feasible decision that would be (approximately) optimal given those preferences. We argue for the importance of assessing numerical utilities rather than qualitative preferences and survey several utility elicitation techniques from artificial intelligence, operations research, and conjoint analysis. Specifically, since the ability to make reasonable decisions on behalf of a user depends on that user's preferences over outcomes in the domain in question, AI systems must assess or estimate these preferences before making decisions. Designing effective preference assessment techniques to incorporate such user-specific considerations (that is, breaking the preference bottleneck) is one of the most important problems facing AI. In this brief survey, we focus on explicit elicitation techniques where a system actively queries a user to glean relevant preferences. Preference elicitation is difficult for two main reasons. First, many decision problems have exponentially sized outcome spaces, defined by the possible values of outcome attributes. As an illustrative example, consider sophisticated flight selection: possible outcomes are defined by attributes such as trip cost, departure time, return time, airline, number of connections, flight length, baggage weight limit, flight class, (the possibility of) lost luggage, flight delays, and other stochastic outcomes. An ideal decision support system should be able to use, for example, precise flight delay statistics and incorporate a user's relative tolerance for delays in making recommendations. Representing and eliciting preferences for all outcomes in a case like this is infeasible given the size of the outcome space. A second difficulty arises due to the fact that quantitative strength of preferences, or utility, is needed to trade off, for instance, the odds of flight delays with other attributes. Unfortunately, people are notoriously inept at quantifying their preferences with any degree of precision, adding to the challenges facing automated utility elicitation.

It involves choosing optimally between different lines of action in various information contexts that range from perfectly knowing all aspects of the decision problem to having just partial knowledge about it. The physical context often includes other interacting autonomous systems, typically called agents. In this article, I focus on decision making in a multiagent context with partial information about the problem. Relevant research in this complex but realistic setting has converged around two complementary, general frameworks and also introduced myriad specializations on its way. I put the two frameworks, decentralized partially observable Markov decision process (Dec-POMDP) and the interactive partially observable Markov decision process (I-POMDP), in context and review the foundational algorithms for these frameworks, while briefly discussing the advances in their specializations.

Decision analysis and knowledge-based expert systems share some common goals. Both technologies are designed to improve human decision making; they attempt to do this by formalizing human expert knowledge so that it is amenable to mechanized reasoning. However, the technologies are based on rather different principles. Decision analysis is the application of the principles of decision theory supplemented with insights from the psychology of judgment. Expert systems, at least as we use this term here, involve the application of various logical and computational techniques of AI to the representation of human knowledge for automated inference.

Over the last few years, a method of reasoning using probabilities, variously called belief networks, Bayesian networks, knowledge maps, probabilistic causal networks, and so on, has become popular within the AI probability and uncertainty community. This method is best summarized in Judea Pearl's (1988) book, but the ideas are a product of many hands. I adopted Pearl's name, Bayesian networks, on the grounds that the name is completely neutral about the status of the networks (do they really represent beliefs, causality, or what?). I give an introduction to Bayesian networks for AI researchers with a limited grounding in probability theory. Over the last few years, this method of reasoning using probabilities has become popular within the AI probability and uncertainty community.

This article provides an overview of the field of qualitative decision theory: its motivating tasks and issues, its antecedents, and its prospects. Qualitative decision theory studies qualitative approaches to problems of decision making and their sound and effective reconciliation and integration with quantitative approaches. Although it inherits from a long tradition, the field offers a new focus on a number of important unanswered questions of common concern to AI, economics, law, psychology, and management. As developed by philosophers, economists, and mathematicians over some 300 years, these disciplines have developed many powerful ideas and techniques, which exert major influences over virtually all the biological, cognitive, and social sciences. Their uses range from providing mathematical foundations for microeconomics to daily application in a range of fields of practice, including finance, public policy, medicine, and now even automated device diagnosis.

Foremost Manufacturing Inc. (Union, NJ), a manufacturer of reflectors for lighting fixtures, has adopted a fuzzy logic-based application to produce quotations for customers in less time. The company is using a fuzzy system to produce bids in about 1.5 minutes, compared to an industry average of two weeks. Carnegie Group Inc. (Pittsburgh, PA) has developed a hybrid neural network/expert system for diagnostic situations where signal data and symbolic data must be combined to perform a definitive diagnosis and repair procedure. This technology was developed with funding from the National Science Foundation. Working with experts from Armco Steel (Middletown, OH), Carnegie Group developed a prototype system to diagnose chatter in a coldrolling mill.