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Efficient Lifting for Online Probabilistic Inference
 PROCEEDINGS OF THE TWENTYFOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI10)
, 2010
"... Lifting can greatly reduce the cost of inference on firstorder probabilistic graphical models, but constructing the lifted network can itself be quite costly. In online applications (e.g., video segmentation) repeatedly constructing the lifted network for each new inference can be extremely wasteful ..."
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Cited by 8 (1 self)
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Lifting can greatly reduce the cost of inference on firstorder probabilistic graphical models, but constructing the lifted network can itself be quite costly. In online applications (e.g., video segmentation) repeatedly constructing the lifted network for each new inference can be extremely wasteful, because the evidence typically changes little from one inference to the next. The same is true in many other problems that require repeated inference, like utility maximization, MAP inference, interactive inference, parameter and structure learning, etc. In this paper, we propose an efficient algorithm for updating the structure of an existing lifted network with incremental changes to the evidence. This allows us to construct the lifted network once for the initial inference
DTProbLog: A decisiontheoretic probabilistic Prolog
 In Proceedings of the TwentyFourth National Conference on Artificial Intelligence, AAAI10
, 2010
"... We introduce DTPROBLOG, a decisiontheoretic extension of Prolog and its probabilistic variant ProbLog. DTPROBLOG is a simple but expressive probabilistic programming language that allows the modeling of a wide variety of domains, such as viral marketing. In DTPROBLOG, the utility of a strategy ..."
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Cited by 7 (4 self)
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We introduce DTPROBLOG, a decisiontheoretic extension of Prolog and its probabilistic variant ProbLog. DTPROBLOG is a simple but expressive probabilistic programming language that allows the modeling of a wide variety of domains, such as viral marketing. In DTPROBLOG, the utility of a strategy (a particular choice of actions) is defined as the expected reward for its execution in the presence of probabilistic effects. The key contribution of this paper is the introduction of exact, as well as approximate, solvers to compute the optimal strategy for a DTPROBLOG program and the decision problem it represents, by making use of binary and algebraic decision diagrams. We also report on experimental results that show the effectiveness and the practical usefulness of the approach. 1.
Context Aware Decision System in a Smart Home: Knowledge Representation and Decision Making Using Uncertain Contextual Information
"... Abstract. This research addresses the issue of building home automation systems reactive to voice for improved comfort and autonomy at home. The paper presents a complete framework that acquires data from sensors and interprets them, by means of IA techniques, to provide contextual information for d ..."
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Cited by 3 (2 self)
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Abstract. This research addresses the issue of building home automation systems reactive to voice for improved comfort and autonomy at home. The paper presents a complete framework that acquires data from sensors and interprets them, by means of IA techniques, to provide contextual information for decision making. The system uses a twolevel ontology to represent the di erent concepts handled during the processing which also contains SWRL instances to automatise some of the reasoning. The focus of this paper is on the relationship between the knowledge representation and the decision process which uses a dedicated Markov Logic Network approach to bene t from the formal logical de nition of decision rules as well as the ability to handle uncertain facts inferred from real data. The entire approach is situated w.r.t. the Sweet Home project whose aim is to make possible contextaware voice command at home.
Proceedings of the TwentySixth AAAI Conference on Artificial Intelligence Lifted MEU by Weighted Model Counting
"... Recent work in the field of probabilistic inference demonstrated the efficiency of weighted model counting (WMC) engines for exact inference in propositional and, very recently, first order models. To date, these methods have not been applied to decision making models, propositional or first order, ..."
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Recent work in the field of probabilistic inference demonstrated the efficiency of weighted model counting (WMC) engines for exact inference in propositional and, very recently, first order models. To date, these methods have not been applied to decision making models, propositional or first order, such as influence diagrams, and Markov decision networks (MDN). In this paper we show how this technique can be applied to such models. First, we show how WMC can be used to solve (propositional) MDNs. Then, we show how this can be extended to handle a firstorder model – the Markov Logic Decision Network (MLDN). WMC offers two central benefits: it is a very simple and very efficient technique. This is particularly true for the firstorder case, where the WMC approach is simpler conceptually, and, in many cases, more effective computationally than the existing methods for solving MLDNs via firstorder variable elimination, or via propositionalization. We demonstrate the above empirically.
(EMBC'13), Osaka: Japan (2013)" The SweetHome Project: Audio Processing and Decision Making in Smart Home to Improve Wellbeing and Reliance
, 2014
"... audiobased interaction technology that lets the user have full control over their home environment, at detecting distress situations and at easing the social inclusion of the elderly and frail population. This paper presents an overview of the project focusing on the implemented techniques for spee ..."
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audiobased interaction technology that lets the user have full control over their home environment, at detecting distress situations and at easing the social inclusion of the elderly and frail population. This paper presents an overview of the project focusing on the implemented techniques for speech and sound recognition as contextaware decision making with uncertainty. A user experiment in a smart home demonstrates the interest of this audiobased technology. I.
Towards Adversarial Reasoning in Statistical Relational Domains
"... Statistical relational artificial intelligence combines firstorder logic and probability in order to handle the complexity and uncertainty present in many realworld domains. However, many realworld domains also include multiple agents that cooperate or compete according to their diverse goals. ..."
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Statistical relational artificial intelligence combines firstorder logic and probability in order to handle the complexity and uncertainty present in many realworld domains. However, many realworld domains also include multiple agents that cooperate or compete according to their diverse goals. In order to handle such domains, an autonomous agent must also consider the actions of other agents. In this paper, we show that existing statistical relational modeling and inference techniques can be readily adapted to certain adversarial or noncooperative scenarios. We also discuss how learning methods can be adapted to be robust to the behavior of adversaries. Extending and applying these methods to realworld problems will extend the scope and impact of statistical relational artificial intelligence.
Markov Logic: A Language and Algorithms for Link Mining
"... Abstract. Link mining problems are characterized by high complexity (since linked objects are not statistically independent) and uncertainty (since data is noisy and incomplete). Thus they necessitate a modeling language that is both probabilistic and relational. Markov logic provides this by attach ..."
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Abstract. Link mining problems are characterized by high complexity (since linked objects are not statistically independent) and uncertainty (since data is noisy and incomplete). Thus they necessitate a modeling language that is both probabilistic and relational. Markov logic provides this by attaching weights to formulas in firstorder logic and viewing them as templates for features of Markov networks. Many link mining problems can be elegantly formulated and efficiently solved using Markov logic. Inference algorithms for Markov logic draw on ideas from satisfiability testing, Markov chain Monte Carlo, belief propagation and resolution. Learning algorithms are based on convex optimization, pseudolikelihood and inductive logic programming. Markov logic has been used successfully in a wide variety of link mining applications, and is the basis of the opensource Alchemy system. 1
held at the European Conference on Artificial Intelligence Editors
, 2012
"... Dealing with context is one of the most interesting and important problems faced in Artificial Intelligence (AI). Traditional AI applications often require to model, store, retrieve and reason about knowledge that holds within certain circumstances—the context. Without considering this contextual in ..."
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Dealing with context is one of the most interesting and important problems faced in Artificial Intelligence (AI). Traditional AI applications often require to model, store, retrieve and reason about knowledge that holds within certain circumstances—the context. Without considering this contextual information, reasoning can easily run into problems such as: inconsistency, when considering knowledge in the wrong context; inefficiency, by considering knowledge irrelevant for a certain context; incompleteness, since an inference may depend on knowledge assumed to hold for a context but which is not explicitly stated. Contextual information is also relevant in knowledge representation and reasoning and it represents a strategic aspect to deal with inconsistency, ambiguity, uncertainty, knowledge base evolution, and commonsense reasoning, among others. In recent years, research in contextaware knowledge representation and reasoning became more relevant in the areas of Semantic Web and Intelligent Systems, where knowledge is not considered a monolithic and static asset, but it is distributed in a network of interconnected heterogeneous and evolving knowledge resources. The ARCOE workshop aims to provide a dedicated forum for researchers interested in these topics to discuss recent developments, important open issues, and future directions.
3. DATES COVERED 4. TITLE AND SUBTITLE ResouceBounded Information Acquisition And Learning
, 2012
"... Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments ..."
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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 222024302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.