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52
Toward a Logic for Qualitative Decision Theory
 In Proceedings of the KR'94
, 1992
"... We present a logic for representing and reasoning with qualitative statements of preference and normality and describe how these may interact in decision making under uncertainty. Our aim is to develop a logical calculus that employs the basic elements of classical decision theory, namely proba ..."
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Cited by 196 (4 self)
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We present a logic for representing and reasoning with qualitative statements of preference and normality and describe how these may interact in decision making under uncertainty. Our aim is to develop a logical calculus that employs the basic elements of classical decision theory, namely probabilities, utilities and actions, but exploits qualitative information about these elements directly for the derivation of goals. Preferences and judgements of normality are captured in a modal/conditional logic, and a simple model of action is incorporated. Without quantitative information, decision criteria other than maximum expected utility are pursued. We describe how techniques for conditional default reasoning can be used to complete information about both preferences and normality judgements, and we show how maximin and maximax strategies can be expressed in our logic.
Planning for Contingencies: A Decisionbased Approach
, 1996
"... A fundamental assumption made by classical AI planners is that there is no uncertainty in the world: the planner has full knowledge of the conditions under which the plan will be executed and the outcome of every action is fully predictable. These planners cannot therefore construct contingency p ..."
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Cited by 99 (3 self)
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A fundamental assumption made by classical AI planners is that there is no uncertainty in the world: the planner has full knowledge of the conditions under which the plan will be executed and the outcome of every action is fully predictable. These planners cannot therefore construct contingency plans, i.e., plans in which different actions are performed in different circumstances. In this paper we discuss some issues that arise in the representation and construction of contingency plans and describe Cassandra, a partialorder contingency planner. Cassandra uses explicit decisionsteps that enable the agent executing the plan to decide which plan branch to follow. The decisionsteps in a plan result in subgoals to acquire knowledge, which are planned for in the same way as any other subgoals. Cassandra thus distinguishes the process of gathering information from the process of making decisions. The explicit representation of decisions in Cassandra allows a coherent approach to...
An Algorithm for Probabilistic LeastCommitment Planning
, 1994
"... We define the probabilistic planning problem in terms of a probability distribution over initial world states, a boolean combination of goal propositions, a probability threshold, and actions whose effects depend on the executiontime state of the world and on random chance. Adopting a probabilistic ..."
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Cited by 83 (2 self)
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We define the probabilistic planning problem in terms of a probability distribution over initial world states, a boolean combination of goal propositions, a probability threshold, and actions whose effects depend on the executiontime state of the world and on random chance. Adopting a probabilistic model complicates the definition of plan success: instead of demanding a plan that provably achieves the goal, we seek plans whose probability of success exceeds the threshold. This paper describes a probabilistic semantics for planning under uncertainty, and presents a fully implemented algorithm that generates plans that succeed with probability no less than a usersupplied probability threshold. The algorithm is sound (if it terminates then the generated plan is sufficiently likely to achieve the goal) and complete (the algorithm will generate a solution if one exists).
The First Law of Robotics (a call to arms)
, 1994
"... Even before the advent of Artificial Intelligence, science fiction writer Isaac Asimov recognized that an agent must place the protection of humans from harm at a higher priority than obeying human orders. Inspired by Asimov, we pose the following fundamental questions: (1) How should one formalize ..."
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Cited by 55 (11 self)
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Even before the advent of Artificial Intelligence, science fiction writer Isaac Asimov recognized that an agent must place the protection of humans from harm at a higher priority than obeying human orders. Inspired by Asimov, we pose the following fundamental questions: (1) How should one formalize the rich, but informal, notion of "harm"? (2) How can an agent avoid performing harmful actions, and do so in a computationally tractable manner? (3) How should an agent resolve conflict between its goals and the need to avoid harm? (4) When should an agent prevent a human from harming herself? While we address some of these questions in technical detail, the primary goal of this paper is to focus attention on Asimov's concern: society will reject autonomous agents unless we have some credible means of making them safe! The Three Laws of Robotics: 1. A robot may not injure a human being, or, through inaction, allow a human being to come to harm. 2. A robot must obey orders given it by human ...
Planning with qualitative temporal preferences
 In KR
, 2006
"... In this paper, we address the problem of specifying and generating preferred plans using rich, qualitative user preferences. We propose a logical language for specifying nonMarkovian preferences over the evolution of states and actions associated with a plan. The semantics for our firstorder prefe ..."
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Cited by 38 (11 self)
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In this paper, we address the problem of specifying and generating preferred plans using rich, qualitative user preferences. We propose a logical language for specifying nonMarkovian preferences over the evolution of states and actions associated with a plan. The semantics for our firstorder preference language is defined in the situation calculus. Unlike other recent temporal preference languages, our preferences are qualitative rather than just ordinal, affording greater expressivity and less incomparability. We propose an approach to computing preferred plans via bounded bestfirst search in a forwardchaining planner. Key components of our approach are the exploitation of progression to efficiently evaluate levels of preference satisfaction over partial plans, and development of an admissible evaluation function that establishes the optimality of bestfirst search. We have implemented our planner PPLAN and evaluated it experimentally. Our preference language and planning approach is amenable to integration with several existing planners, and beyond planning, can be used to support arbitrary dynamical reasoning tasks involving preferences. 1
Rewarding behaviors
 In Proceedings of the Thirteenth National Conferenceon Artificial Intelligence
, 1996
"... Markov decision processes (MDPs) are a very popular tool for decision theoretic planning (DTP), partly because of the welldeveloped, expressive theory that includes effective solution techniques. But the Markov assumption—that dynamics and rewards depend on the current state only, and not on history ..."
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Cited by 30 (2 self)
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Markov decision processes (MDPs) are a very popular tool for decision theoretic planning (DTP), partly because of the welldeveloped, expressive theory that includes effective solution techniques. But the Markov assumption—that dynamics and rewards depend on the current state only, and not on history— is often inappropriate. This is especially true of rewards: we frequently wish to associate rewards with behaviors that extend over time. Of course, such reward processes can be encoded in an MDP should we have a rich enough state space (where states encode enough history). However it is often difficult to “hand craft ” suitable state spaces that encode an appropriate amount of history. We consider this problem in the case where nonMarkovian rewards are encoded by assigning values to formulas of a temporal logic. These formulas characterize the value of temporally extended behaviors. We argue that this allows a natural representation of many commonly encountered nonMarkovian rewards. The main result is an algorithm which, given a decision process with nonMarkovian rewards expressed in this manner, automatically constructs an equivalent MDP (with Markovian reward structure), allowing optimal policy construction using standard techniques. 1
Expressive power of weighted propositional formulas for cardinal preference modeling
 In Proceedings of KR’07
, 2007
"... As proposed in various places, a set of propositional formulas, each associated with a numerical weight, can be used to model the preferences of an agent in combinatorial domains. If the range of possible choices can be represented by the set of possible assignments of propositional symbols to truth ..."
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Cited by 27 (5 self)
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As proposed in various places, a set of propositional formulas, each associated with a numerical weight, can be used to model the preferences of an agent in combinatorial domains. If the range of possible choices can be represented by the set of possible assignments of propositional symbols to truth values, then the utility of an assignment is given by the sum of the weights of the formulas it satisfies. Our aim in this paper is twofold: (1) to establish correspondences between certain types of weighted formulas and wellknown classes of utility functions (such as monotonic, concave or kadditive functions); and (2) to obtain results on the comparative succinctness of different types of weighted formulas for representing the same class of utility functions.
Theoretical Foundations for AbstractionBased Probabilistic Planning
 In Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence
, 1996
"... ionBased Probabilistic Planning Vu Ha Peter Haddawy Department of EE & CS University of WisconsinMilwaukee fvu, haddawyg@cs.uwm.edu Abstract Modeling worlds and actions under uncertainty is one of the central problems in the framework of decisiontheoretic planning. The representation must be ..."
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Cited by 27 (3 self)
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ionBased Probabilistic Planning Vu Ha Peter Haddawy Department of EE & CS University of WisconsinMilwaukee fvu, haddawyg@cs.uwm.edu Abstract Modeling worlds and actions under uncertainty is one of the central problems in the framework of decisiontheoretic planning. The representation must be general enough to capture realworld problems but at the same time it must provide a basis upon which theoretical results can be derived. The central notion in the framework we propose here is that of the affineoperator, which serves as a tool for constructing (convex) sets of probability distributions, and which can be considered as a generalization of belief functions and interval mass assignments. Uncertainty in the state of the worlds is modeled with sets of probability distributions, represented by affinetrees, while actions are defined as treemanipulators. A small set of key properties of the affineoperator is presented, forming the basis for most existing operatorbased definitio...
Structured solution methods for nonMarkovian decision processes
 In Proceedings of the Fourteenth National Conference on Artificial Intelligence (AAAI97
, 1997
"... Markov Decision Processes (MDPs), currently a popular method for modeling and solving decision theoretic planning problems, are limited by the Markovian assumption: rewards and dynamics depend on the current state only, and not on previous history. NonMarkovian decision processes (NMDPs) can also b ..."
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Cited by 25 (1 self)
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Markov Decision Processes (MDPs), currently a popular method for modeling and solving decision theoretic planning problems, are limited by the Markovian assumption: rewards and dynamics depend on the current state only, and not on previous history. NonMarkovian decision processes (NMDPs) can also be defined, but then the more tractable solution techniques developed for MDP’s cannot be directly applied. In this paper, we show how an NMDP, in which temporal logic is used to specify history dependence, can be automatically converted into an equivalent MDP by adding appropriate temporal variables. The resulting MDP can be represented in a structured fashion and solved using structured policy construction methods. In many cases, this offers significant computational advantagesover previous proposals for solving NMDPs. 1