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73
The BOID Architecture  Conflicts Between Beliefs, Obligations, Intentions and Desires
 IN PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS
, 2001
"... In this paper we introduce the socalled BeliefsObligationsIntentionsDesires or BOID architecture. It contains feedback loops to consider all eects of actions before committing to them, and mechanisms to resolve conflicts between the outputs of its four components. Agent types such as realistic o ..."
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Cited by 65 (12 self)
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In this paper we introduce the socalled BeliefsObligationsIntentionsDesires or BOID architecture. It contains feedback loops to consider all eects of actions before committing to them, and mechanisms to resolve conflicts between the outputs of its four components. Agent types such as realistic or social agents correspond to specific types of conflict resolution embedded in the BOID architecture.
Independence and Qualitative Decision Theory
 In Proceedings of KR'96
, 1997
"... Probabilistic independence has proved to be a fundamental tool that can dramatically simplify the task of eliciting, representing, and computing with probabilities. We advance the position that notions of utility independence can serve similar functions when reasoning about preferences and uti ..."
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Cited by 57 (0 self)
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Probabilistic independence has proved to be a fundamental tool that can dramatically simplify the task of eliciting, representing, and computing with probabilities. We advance the position that notions of utility independence can serve similar functions when reasoning about preferences and utilities during decision making.
Counterfactual Probabilities: Computational Methods, Bounds and Applications
 UNCERTAINTY IN ARTIFICIAL INTELLIGENCE
, 1994
"... Evaluation of counterfactual queries (e.g., "If A were true, would C have been true?") is important to fault diagnosis, planning, and determination of liability. In this paper we present methods for computing the probabilities of such queries using the formulation proposed in [Balke and P ..."
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Cited by 56 (20 self)
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Evaluation of counterfactual queries (e.g., "If A were true, would C have been true?") is important to fault diagnosis, planning, and determination of liability. In this paper we present methods for computing the probabilities of such queries using the formulation proposed in [Balke and Pearl, 1994], where the antecedent of the query is interpreted as an external action that forces the proposition A to be true. When a prior probability is available on the causal mechanisms governing the domain, counterfactual probabilities can be evaluated precisely. However, when causal knowledge is specified as conditional probabilities on the observables, only bounds can computed. This paper develops techniques for evaluating these bounds, and demonstrates their use in two applications: (1) the determination of treatment efficacy from studies in which subjects may choose their own treatment, and (2) the determination of liability in productsafety litigation.
Probabilistic Evaluation of Counterfactual Queries
 IN PROCEEDINGS AAAI94
, 1994
"... Evaluation of counterfactual queries (e.g., "If A were true, would C have been true?") is important to fault diagnosis, planning, and determination of liability. We present a formalism that uses probabilistic causal networks to evaluate one's belief that the counterfactual consequent, ..."
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Cited by 50 (15 self)
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Evaluation of counterfactual queries (e.g., "If A were true, would C have been true?") is important to fault diagnosis, planning, and determination of liability. We present a formalism that uses probabilistic causal networks to evaluate one's belief that the counterfactual consequent, C, would have been true if the antecedent, A, were true. The antecedent of the query is interpreted as an external action that forces the proposition A to be true, which is consistent with Lewis' Miraculous Analysis. This formalism offers a concrete embodiment of the "closest world" approach which (1) properly reflects common understanding of causal influences, (2) deals with the uncertainties inherent in the world, and (3) is amenable to machine representation.
ContraryToDuty Reasoning with Preferencebased Dyadic Obligations
, 1999
"... this paper we introduce Prohairetic Deontic Logic (PDL), a preferencebased ..."
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Cited by 46 (21 self)
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this paper we introduce Prohairetic Deontic Logic (PDL), a preferencebased
Arguing for Decisions: A Qualitative Model of Decision Making
 In Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence
, 1996
"... We develop a qualitative model of decision making with two aims: to describe how people make simple decisions and to enable computer programs to do the same. Current approaches based on Planning or Decision Theory either ignore uncertainty and tradeoffs, or provide languages and algorithms tha ..."
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Cited by 37 (0 self)
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We develop a qualitative model of decision making with two aims: to describe how people make simple decisions and to enable computer programs to do the same. Current approaches based on Planning or Decision Theory either ignore uncertainty and tradeoffs, or provide languages and algorithms that are too complex for this task. The proposed model provides a language based on rules, a semantics based on high probabilities and lexicographical preferences, and a transparent decision procedure where reasons for and against decisions interact. The model is no substitute for Decision Theory, yet for decisions that people find easy to explain it may provide an appealing alternative.
mGPT: A probabilistic planner based on heuristic search
 Journal of Artificial Intelligence Research
, 2005
"... We describe the version of the GPT planner to be used in the planning competition. This version, called mGPT, solves mdps specified in the ppddl language by extracting and using different classes of lower bounds, along with various heuristicsearch algorithms. The lower bounds are extracted from det ..."
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Cited by 34 (0 self)
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We describe the version of the GPT planner to be used in the planning competition. This version, called mGPT, solves mdps specified in the ppddl language by extracting and using different classes of lower bounds, along with various heuristicsearch algorithms. The lower bounds are extracted from deterministic relaxations of the mdp where alternative probabilistic effects of an action are mapped into different, independent, deterministic actions. The heuristicsearch algorithms, on the other hand, use these lower bounds for focusing the updates and delivering a consistent value function over all states reachable from the initial state with the greedy policy.
On the Complexity of Conditional Logics
 In Principles of Knowledge Representation and Reasoning: Proc. Fourth International Conference (KR '94
, 1994
"... Conditional logics, introduced by Lewis and Stalnaker, have been utilized in artificial intelligence to capture a broad range of phenomena. In this paper we examine the complexity of several variants discussed in the literature. We show that, in general, deciding satisfiability is PSPACEcomplete fo ..."
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Cited by 34 (5 self)
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Conditional logics, introduced by Lewis and Stalnaker, have been utilized in artificial intelligence to capture a broad range of phenomena. In this paper we examine the complexity of several variants discussed in the literature. We show that, in general, deciding satisfiability is PSPACEcomplete for formulas with arbitrary conditional nesting and NPcomplete for formulas with bounded nesting of conditionals. However, we provide several exceptions to this rule. Of particular note are results showing that (a) when assuming uniformity (i.e., that all worlds agree on what worlds are possible), the decision problem becomes EXPTIMEcomplete even for formulas with bounded nesting, and (b) when assuming absoluteness (i.e., that all worlds agree on all conditional statements), the decision problem is NPcomplete for formulas with arbitrary nesting. 1 INTRODUCTION The study of conditional statements of the form "If : : : then : : :" has a long history in philosophy [Sta68, Lew73, Che80, Vel8...
A Probabilistic Calculus of Actions
, 1994
"... We present a symbolic machinery that admits both probabilistic and causal information about a given domain, and produces probabilistic statements about the effect of actions and the impact of observations. The calculus admits two types of conditioning operators: ordinary Bayes conditioning, P (yj ..."
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Cited by 31 (13 self)
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We present a symbolic machinery that admits both probabilistic and causal information about a given domain, and produces probabilistic statements about the effect of actions and the impact of observations. The calculus admits two types of conditioning operators: ordinary Bayes conditioning, P (yjX = x), which represents the observation X = x, and causal conditioning, P (yjdo(X = x)), read: the probability of Y = y conditioned on holding X constant (at x) by deliberate action. Given a mixture of such observational and causal sentences, together with the topology of the causal graph, the calculus derives new conditional probabilities of both types, thus enabling one to quantify the effects of actions and observations. 1 Introduction Probabilistic methods, especially those based on graphical models have proven useful in tasks of predictions, abduction and belief revision [Pearl 1988, Heckerman 1990, Goldszmidt 1992, Darwiche 1993]. Their use in planning, however, remains less po...
Parameters for Utilitarian Desires in a Qualitative Decision Theory
, 2001
"... In qualitative decisiontheoretic planning, desires—qualitative abstractions of utility functions—are combined with defaults—qualitative abstractions of probability distributions—to calculate the expected utilities of actions. This paper is inspired from Lang’s framework of qualitative decision theo ..."
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Cited by 27 (10 self)
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In qualitative decisiontheoretic planning, desires—qualitative abstractions of utility functions—are combined with defaults—qualitative abstractions of probability distributions—to calculate the expected utilities of actions. This paper is inspired from Lang’s framework of qualitative decision theory, in which utility functions are constructed from desires. Unfortunately, there is no consensus about the desirable logical properties of desires, in contrast to the case for defaults. To do justice to the wide variety of desires we define parameterized desires in an extension of Lang’s framework. We introduce three parameters, which help us to implement different facets of risk. The strength parameter encodes the importance of the desire, the lifting parameter encodes how to determine the utility of a set (proposition) from the utilities of its elements (worlds), and the polarity parameter encodes the relation between gain of utility for rewards and loss of utility for violations. The parameters influence how desires interact, and they thus increase the control on the construction process of utility functions from desires.