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  • Automated construction of bounded-loss imperfect-recall abstractions in extensive-form games
    Artif. Intell. (IF 4.483) Pub Date : 2020-02-14
    Jiří Čermák; Viliam Lisý; Branislav Bošanský

    Extensive-form games (EFGs) model finite sequential interactions between players. The amount of memory required to represent these games is the main bottleneck of algorithms for computing optimal strategies and the size of these strategies is often impractical for real-world applications. A common approach to tackle the memory bottleneck is to use information abstraction that removes parts of information available to players thus reducing the number of decision points in the game. However, existing information-abstraction techniques are either specific for a particular domain, they do not provide any quality guarantees, or they are applicable to very small subclasses of EFGs. We present domain-independent abstraction methods for creating imperfect recall abstractions in extensive-form games that allow computing strategies that are (near) optimal in the original game. To this end, we introduce two novel algorithms, FPIRA and CFR+IRA, based on fictitious play and counterfactual regret minimization. These algorithms can start with an arbitrary domain specific, or the coarsest possible, abstraction of the original game. The algorithms iteratively detect the missing information they require for computing a strategy for the abstract game that is (near) optimal in the original game. This information is then included back into the abstract game. Moreover, our algorithms are able to exploit imperfect-recall abstractions that allow players to forget even history of their own actions. However, the algorithms require traversing the complete unabstracted game tree. We experimentally show that our algorithms can closely approximate Nash equilibrium of large games using abstraction with as little as 0.9% of information sets of the original game. Moreover, the results suggest that memory savings increase with the increasing size of the original games.

    更新日期:2020-02-20
  • How do fairness definitions fare? Testing public attitudes towards three algorithmic definitions of fairness in loan allocations
    Artif. Intell. (IF 4.483) Pub Date : 2020-02-20
    Nripsuta Ani Saxena; Karen Huang; Evan DeFilippis; Goran Radanovic; David C. Parkes; Yang Liu

    What is the best way to define algorithmic fairness? While many definitions of fairness have been proposed in the computer science literature, there is no clear agreement over a particular definition. In this work, we investigate ordinary people's perceptions of three of these fairness definitions. Across three online experiments, we test which definitions people perceive to be the fairest in the context of loan decisions, and whether fairness perceptions change with the addition of sensitive information (i.e., race or gender of the loan applicants). Overall, one definition (calibrated fairness) tends to be more preferred than the others, and the results also provide support for the principle of affirmative action.

    更新日期:2020-02-20
  • Autoepistemic equilibrium logic and epistemic specifications
    Artif. Intell. (IF 4.483) Pub Date : 2020-02-19
    Luis Fariñas del Cerro; Andreas Herzig; Ezgi Iraz Su

    Epistemic specifications extend disjunctive answer-set programs by an epistemic modal operator that may occur in the body of rules. Their semantics is in terms of world views, which are sets of answer sets, and the idea is that the epistemic modal operator quantifies over these answer sets. Several such semantics were proposed in the literature. We here propose a new semantics that is based on the logic of here-and-there: we add epistemic modal operators to its language and define epistemic here-and-there models. We then successively define epistemic equilibrium models and autoepistemic equilibrium models. The former are obtained from epistemic here-and-there models in exactly the same way as Pearce's equilibrium models are obtained from here-and-there models, viz. by minimising truth; they provide an epistemic extension of equilibrium logic. The latter are obtained from the former by maximising the set of epistemic possibilities, and they provide a new semantics for Gelfond's epistemic specifications. For both semantics we establish a strong equivalence result: we characterise strong equivalence of two epistemic programs by means of logical equivalence in epistemic here-and-there logic. We finally compare our approach to the existing semantics of epistemic specifications and discuss which formalisms provide more intuitive results by pointing out some formal properties a semantics proposal should satisfy.

    更新日期:2020-02-20
  • Robust Learning with Imperfect Privileged Information
    Artif. Intell. (IF 4.483) Pub Date : 2020-02-12
    Xue Li; Bo Du; Chang Xu; Yipeng Zhang; Lefei Zhang; Dacheng Tao

    In the learning using privileged information (LUPI) paradigm, example data cannot always be clean, while the gathered privileged information can be imperfect in practice. Here, imperfect privileged information can refer to auxiliary information that is not always accurate or perturbed by noise, or alternatively to incomplete privileged information, where privileged information is only available for part of the training data. Because of the lack of clear strategies for handling noise in example data and imperfect privileged information, existing learning using privileged information (LUPI) methods may encounter serious issues. Accordingly, in this paper, we propose a Robust SVM+ method to tackle imperfect data in LUPI. In order to make the SVM+ model robust to noise in example data and privileged information, Robust SVM+ maximizes the lower bound of the perturbations that may influence the judgement based on a rigorous theoretical analysis. Moreover, in order to deal with the incomplete privileged information, we use the available privileged information to help us in approximating the missing privileged information of training data. The optimization problem of the proposed method can be efficiently solved by employing a two-step alternating optimization strategy, based on iteratively deploying off-the-shelf quadratic programming solvers and the alternating direction method of multipliers (ADMM) technique. Comprehensive experiments on real-world datasets demonstrate the effectiveness of the proposed Robust SVM+ method in handling imperfect privileged information.

    更新日期:2020-02-12
  • Rethinking epistemic logic with belief bases
    Artif. Intell. (IF 4.483) Pub Date : 2020-02-10
    Emiliano Lorini

    We introduce a new semantics for a family of logics of explicit and implicit belief based on the concept of multi-agent belief base. Differently from standard semantics for epistemic logic in which the notions of possible world and doxastic/epistemic alternative are primitive, in our semantics they are non-primitive but are computed from the concept of belief base. We provide complete axiomatizations and prove decidability for our logics via finite model arguments. Furthermore, we provide polynomial embeddings of our logics into Fagin & Halpern's logic of general awareness and establish complexity results via the embeddings. We also present variants of the logics incorporating different forms of epistemic introspection for explicit and/or implicit belief and provide complexity results for some of these variants. Finally, we present a number of dynamic extensions of the static framework by informative actions of both public and private type, including public announcement, belief base expansion and forgetting. We illustrate the application potential of the logical framework with the aid of a concrete example taken from the domain of conversational agents.

    更新日期:2020-02-10
  • Regression and Progression in Stochastic Domains
    Artif. Intell. (IF 4.483) Pub Date : 2020-01-30
    Vaishak Belle; Hector J. Levesque

    Reasoning about degrees of belief in uncertain dynamic worlds is fundamental to many applications, such as robotics and planning, where actions modify state properties and sensors provide measurements, both of which are prone to noise. With the exception of limited cases such as Gaussian processes over linear phenomena, belief state evolution can be complex and hard to reason with in a general way, especially when the agent has to deal with categorical assertions, incomplete information such as disjunctive knowledge, as well as probabilistic knowledge. Among the many approaches for reasoning about degrees of belief in the presence of noisy sensing and acting, the logical account proposed by Bacchus, Halpern, and Levesque is perhaps the most expressive, allowing for such belief states to be expressed naturally as constraints. While that proposal is powerful, the task of how to plan effectively is not addressed. In fact, at a more fundamental level, the task of projection, that of reasoning about beliefs effectively after acting and sensing, is left entirely open. To aid planning algorithms, we study the projection problem in this work. In the reasoning about actions literature, there are two main solutions to projection: regression and progression. Both of these have proven enormously useful for the design of logical agents, essentially paving the way for cognitive robotics. Roughly, regression reduces a query about the future to a query about the initial state. Progression, on the other hand, changes the initial state according to the effects of each action and then checks whether the formula holds in the updated state. In this work, we show how both of these generalize in the presence of degrees of belief, noisy acting and sensing. Our results allow for both discrete and continuous probability distributions to be used in the specification of beliefs and dynamics.

    更新日期:2020-01-31
  • Swarm Intelligence for Self-Organized Clustering
    Artif. Intell. (IF 4.483) Pub Date : 2020-01-28
    Michael C. Thrun; Alfred Ultsch

    Algorithms implementing populations of agents which interact with one another and sense their environment may exhibit emergent behavior such as self-organization and swarm intelligence. Here a swarm system, called Databionic swarm (DBS), is introduced which is able to adapt itself to structures of high-dimensional data characterized by distance and/or density-based structures in the data space. By exploiting the interrelations of swarm intelligence, self-organization and emergence, DBS serves as an alternative approach to the optimization of a global objective function in the task of clustering. The swarm omits the usage of a global objective function and is parameter-free because it searches for the Nash equilibrium during its annealing process. To our knowledge, DBS is the first swarm combining these approaches and its clustering can outperform common clustering methods such as K-means, PAM, single linkage, spectral clustering, model-based clustering, and Ward if no prior knowledge about the data is available. A central problem in clustering is the correct estimation of the number of clusters. This is addressed by a DBS visualization called topographic map which allows assessing the number of clusters. It is known that all clustering algorithms construct clusters, no matter if the data set contains clusters or not. In contrast to most other clustering algorithms, the topographic map identifies that clustering of the data is meaningless if the data contains no (natural) clusters. The performance of DBS is demonstrated on a set of benchmark data constructed to pose difficult clustering problems and in two real-world applications.

    更新日期:2020-01-29
  • Ethical approaches and autonomous systems
    Artif. Intell. (IF 4.483) Pub Date : 2020-01-21
    T.J.M. Bench-Capon

    In this paper we consider how the three main approaches to ethics – deontology, consequentialism and virtue ethics – relate to the implementation of ethical agents. We provide a description of each approach and how agents might be implemented by designers following the different approaches. Although there are numerous examples of agents implemented within the consequentialist and deontological approaches, this is not so for virtue ethics. We therefore propose a novel means of implementing agents within the virtue ethics approach. It is seen that each approach has its own particular strengths and weaknesses when considered as the basis for implementing ethical agents, and that the different approaches are appropriate to different kinds of system.

    更新日期:2020-01-22
  • Epistemic graphs for representing and reasoning with positive and negative influences of arguments
    Artif. Intell. (IF 4.483) Pub Date : 2020-01-13
    Anthony Hunter; Sylwia Polberg; Matthias Thimm

    This paper introduces epistemic graphs as a generalization of the epistemic approach to probabilistic argumentation. In these graphs, an argument can be believed or disbelieved up to a given degree, thus providing a more fine–grained alternative to the standard Dung's approaches when it comes to determining the status of a given argument. Furthermore, the flexibility of the epistemic approach allows us to both model the rationale behind the existing semantics as well as completely deviate from them when required. Epistemic graphs can model both attack and support as well as relations that are neither support nor attack. The way other arguments influence a given argument is expressed by the epistemic constraints that can restrict the belief we have in an argument with a varying degree of specificity. The fact that we can specify the rules under which arguments should be evaluated and we can include constraints between unrelated arguments permits the framework to be more context–sensitive. It also allows for better modelling of imperfect agents, which can be important in multi–agent applications.

    更新日期:2020-01-13
  • Story embedding: Learning distributed representations of stories based on character networks
    Artif. Intell. (IF 4.483) Pub Date : 2020-01-13
    O-Joun Lee; Jason J. Jung

    This study aims to learn representations of stories in narrative works (i.e., creative works that contain stories) using fixed-length vectors. Vector representations of stories enable us to compare narrative works regardless of their media or formats. To computationally represent stories, we focus on social networks among characters (character networks). We assume that the structural features of the character networks reflect the characteristics of stories. By extending substructure-based graph embedding models, we propose models to learn distributed representations of character networks in stories. The proposed models consist of three parts: (i) discovering substructures of character networks, (ii) embedding each substructure (Char2Vec), and (iii) learning vector representations of each character network (Story2Vec). We find substructures around each character in multiple scales based on proximity between characters. We suppose that a character's substructures signify its ‘social roles’. Subsequently, a Char2Vec model is designed to embed a social role based on co-occurred social roles. Since character networks are dynamic social networks that temporally evolve, we use temporal changes and adjacency of social roles to determine their co-occurrence. Finally, Story2Vec models predict occurrences of social roles in each story for embedding the story. To predict the occurrences, we apply two approaches: (i) considering temporal changes in social roles as with the Char2Vec model and (ii) focusing on the final social roles of each character. We call the embedding model with the first approach ‘flow-oriented Story2Vec.’ This approach can reflect the context and flow of stories if the dynamics of character networks is well understood. Second, based on the final states of social roles, we can emphasize the denouement of stories, which is an overview of the static structure of the character networks. We name this model as ‘denouement-oriented Story2Vec.’ In addition, we suggest ‘unified Story2Vec’ as a combination of these two models. We evaluated the quality of vector representations generated by the proposed embedding models using movies in the real world.

    更新日期:2020-01-13
  • Synchronous bidirectional inference for neural sequence generation
    Artif. Intell. (IF 4.483) Pub Date : 2020-01-08
    Jiajun Zhang; Long Zhou; Yang Zhao; Chengqing Zong

    In sequence to sequence generation tasks (e.g. machine translation and abstractive summarization), inference is generally performed in a left-to-right manner to produce the result token by token. The neural approaches, such as LSTM and self-attention networks, are now able to make full use of all the predicted history hypotheses from left side during inference, but cannot meanwhile access any future (right side) information and usually generate unbalanced outputs (e.g. left parts are much more accurate than right ones in Chinese-English translation). In this work, we propose a synchronous bidirectional inference model to generate outputs using both left-to-right and right-to-left decoding simultaneously and interactively. First, we introduce a novel beam search algorithm that facilitates synchronous bidirectional decoding. Then, we present the core approach which enables left-to-right and right-to-left decoding to interact with each other, so as to utilize both the history and future predictions simultaneously during inference. We apply the proposed model to both LSTM and self-attention networks. Furthermore, we propose a novel fine-tuning based parameter optimization algorithm in addition to the simple two-pass strategy. The extensive experiments on machine translation and abstractive summarization demonstrate that our synchronous bidirectional inference model can achieve remarkable improvements over the strong baselines.

    更新日期:2020-01-09
  • Definability for model counting
    Artif. Intell. (IF 4.483) Pub Date : 2020-01-07
    Jean-Marie Lagniez; Emmanuel Lonca; Pierre Marquis

    We define and evaluate a new preprocessing technique for propositional model counting. This technique leverages definability, i.e., the ability to determine that some gates are implied by the input formula Σ. Such gates can be exploited to simplify Σ without modifying its number of models. Unlike previous techniques based on gate detection and replacement, gates do not need to be made explicit in our approach. Our preprocessing technique thus consists of two phases: computing a bipartition 〈I,O〉 of the variables of Σ where the variables from O are defined in Σ in terms of I, then eliminating some variables of O in Σ. Our experiments show the computational benefits which can be achieved by taking advantage of our preprocessing technique for model counting.

    更新日期:2020-01-07
  • Relative inconsistency measures
    Artif. Intell. (IF 4.483) Pub Date : 2020-01-07
    Philippe Besnard; John Grant

    The literature on inconsistency measures has ignored a distinction, that is, differentiating absolute measures and relative measures. An absolute measure gives the total amount of inconsistency in the knowledge base but a relative measure computes, by some criteria, the proportion of the base that is inconsistent. To compare the inconsistency measures, researchers have proposed postulates for such measures. We split these postulates into three groups: ones (including two new postulates) that relative measures should satisfy, ones inappropriate for relative measures, and ones that relative measures may satisfy. We obtain some new results upon the relationships between these groups of postulates. On these grounds, we introduce a formal definition for relative inconsistency measures. We consider some relative measures previously proposed and define several new ones that serve as examples. We show that all of these measures satisfy the new formal definition.

    更新日期:2020-01-07
  • The Hanabi challenge: A new frontier for AI research
    Artif. Intell. (IF 4.483) Pub Date : 2019-11-27
    Nolan Bard; Jakob N. Foerster; Sarath Chandar; Neil Burch; Marc Lanctot; H. Francis Song; Emilio Parisotto; Vincent Dumoulin; Subhodeep Moitra; Edward Hughes; Iain Dunning; Shibl Mourad; Hugo Larochelle; Marc G. Bellemare; Michael Bowling

    From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making. In recent years, machine learning has made dramatic advances with artificial agents reaching superhuman performance in challenge domains like Go, Atari, and some variants of poker. As with their predecessors of chess, checkers, and backgammon, these game domains have driven research by providing sophisticated yet well-defined challenges for artificial intelligence practitioners. We continue this tradition by proposing the game of Hanabi as a new challenge domain with novel problems that arise from its combination of purely cooperative gameplay with two to five players and imperfect information. In particular, we argue that Hanabi elevates reasoning about the beliefs and intentions of other agents to the foreground. We believe developing novel techniques for such theory of mind reasoning will not only be crucial for success in Hanabi, but also in broader collaborative efforts, especially those with human partners. To facilitate future research, we introduce the open-source Hanabi Learning Environment, propose an experimental framework for the research community to evaluate algorithmic advances, and assess the performance of current state-of-the-art techniques.

    更新日期:2020-01-04
  • When autonomous agents model other agents: An appeal for altered judgment coupled with mouths, ears, and a little more tape
    Artif. Intell. (IF 4.483) Pub Date : 2019-12-16
    Jacob W. Crandall

    Agent modeling has rightfully garnered much attention in the design and study of autonomous agents that interact with other agents. However, despite substantial progress to date, existing agent-modeling methods too often (a) have unrealistic computational requirements and data needs; (b) fail to properly generalize across environments, tasks, and associates; and (c) guide behavior toward inefficient (myopic) solutions. Can these challenges be overcome? Or are they just inherent to a very complex problem? In this reflection, I argue that some of these challenges may be reduced by, first, modeling alternative processes than what is often modeled by existing algorithms and, second, considering more deeply the role of non-binding communication signals. Additionally, I believe that progress in developing autonomous agents that effectively interact with other agents will be enhanced as we develop and utilize a more comprehensive set of measurement tools and benchmarks. I believe that further development of these areas is critical to creating autonomous agents that effectively model and interact with other agents.

    更新日期:2020-01-04
  • Polynomial rewritings from expressive Description Logics with closed predicates to variants of Datalog
    Artif. Intell. (IF 4.483) Pub Date : 2019-12-16
    Shqiponja Ahmetaj; Magdalena Ortiz; Mantas Šimkus

    In many scenarios, complete and incomplete information coexist. For this reason, the knowledge representation and database communities have long shown interest in simultaneously supporting the closed- and the open-world views when reasoning about logic theories. Here we consider the setting of querying possibly incomplete data using logic theories, formalized as the evaluation of an ontology-mediated query (OMQ) that pairs a query with a theory, sometimes called an ontology, expressing background knowledge. This can be further enriched by specifying a set of closed predicates from the theory that are to be interpreted under the closed-world assumption, while the rest are interpreted with the open-world view. In this way we can retrieve more precise answers to queries by leveraging the partial completeness of the data. The central goal of this paper is to understand the relative expressiveness of ontology-mediated query languages in which the ontology part is written in the expressive Description Logic (DL) ALCHOI and includes a set of closed predicates. We consider a restricted class of conjunctive queries. Our main result is to show that every query in this non-monotonic query language can be translated in polynomial time into Datalog with negation as failure under the stable model semantics. To overcome the challenge that Datalog has no direct means to express the existential quantification present in ALCHOI, we define a two-player game that characterizes the satisfaction of the ontology, and design a Datalog query that can decide the existence of a winning strategy for the game. If there are no closed predicates—in the case of querying an ALCHOI knowledge base—our translation yields a positive disjunctive Datalog program of polynomial size. To the best of our knowledge, unlike previous translations for related fragments with expressive (non-Horn) DLs, these are the first polynomial time translations.

    更新日期:2020-01-04
  • The computational complexity of angry birds
    Artif. Intell. (IF 4.483) Pub Date : 2020-01-03
    Matthew Stephenson; Jochen Renz; Xiaoyu Ge

    The physics-based simulation game Angry Birds has been heavily researched by the AI community over the past five years, and has been the subject of a popular AI competition that is currently held annually as part of a leading AI conference. Developing intelligent agents that can play this game effectively has been an incredibly complex and challenging problem for traditional AI techniques to solve, even though the game is simple enough that any human player could learn and master it within a short time. In this paper we analyse how hard the problem really is, presenting several proofs for the computational complexity of Angry Birds. By using a combination of several gadgets within this game's environment, we are able to demonstrate that the decision problem of solving general levels for different versions of Angry Birds is either NP-hard, PSPACE-hard, PSPACE-complete or EXPTIME-hard. Proof of NP-hardness is by reduction from 3-SAT, whilst proof of PSPACE-hardness is by reduction from True Quantified Boolean Formula (TQBF). Proof of EXPTIME-hardness is by reduction from G2, a known EXPTIME-complete problem similar to that used for many previous games such as Chess, Go and Checkers. To the best of our knowledge, this is the first time that a single-player game has been proven EXPTIME-hard. This is achieved by using stochastic game engine dynamics to effectively model the real world, or in our case the physics simulator, as the opponent against which we are playing. These proofs can also be extended to other physics-based games with similar mechanics.

    更新日期:2020-01-04
  • SCCWalk: An efficient local search algorithm and its improvements for maximum weight clique problem
    Artif. Intell. (IF 4.483) Pub Date : 2020-01-02
    Yiyuan Wang; Shaowei Cai; Jiejiang Chen; Minghao Yin

    The maximum weight clique problem (MWCP) is an important generalization of the maximum clique problem with wide applications. In this study, we develop two efficient local search algorithms for MWCP, namely SCCWalk and SCCWalk4L, where SCCWalk4L is improved from SCCWalk for large graphs. There are two main ideas in SCCWalk, including strong configuration checking (SCC) and walk perturbation. SCC is a new variant of a powerful strategy called configuration checking for local search. The walk perturbation procedure is used to lead the algorithm to leave the current area and come into a new area of feasible solution space. Moreover, to improve the performance on massive graphs, we apply a low-complexity heuristic called best from multiple selection to select the swapping vertex pair quickly and effectively, resulting in the SCCWalk4L algorithm. In addition, SCCWalk4L uses two recent reduction rules to decrease the scale of massive graphs. We carry out experiments to evaluate our algorithms on several popular benchmarks, which are divided into two groups, including classical benchmarks of small graphs namely DIMACS, BHOSLIB, winner determination problem, and graphs derived from clustering aggregation, as well as massive graphs, including a suite of massive real-world graphs and large-scale FRB graphs. Experiments show that, compared to state-of-the-art heuristic algorithms and exact algorithm, the proposed algorithms perform better on classical benchmarks, and obtain the best solutions for most massive graphs.

    更新日期:2020-01-04
  • Reasoning about uncertain parameters and agent behaviors through encoded experiences and belief planning
    Artif. Intell. (IF 4.483) Pub Date : 2019-12-27
    Akinobu Hayashi; Dirk Ruiken; Tadaaki Hasegawa; Christian Goerick

    Robots are expected to handle increasingly complex tasks. Such tasks often include interaction with objects or collaboration with other agents. One of the key challenges for reasoning in such situations is the lack of accurate models that hinders the effectiveness of planners. We present a system for online model adaptation that continuously validates and improves models while solving tasks with a belief space planner. We employ the well known online belief planner POMCP. Particles are used to represent hypotheses about the current state and about models of the world. They are sufficient to configure a simulator to provide transition and observation models. We propose an enhanced particle reinvigoration process that leverages prior experiences encoded in a recurrent neural network (RNN). The network is trained through interaction with a large variety of object and agent parametrizations. The RNN is combined with a mixture density network (MDN) to process the current history of observations in order to propose suitable particles and models parametrizations. The proposed method also ensures that newly generated particles are consistent with the current history. These enhancements to the particle reinvigoration process help alleviate problems arising from poor sampling quality in large state spaces and enable handling of dynamics with discontinuities. The proposed approach can be applied to a variety of domains depending on what uncertainty the decision maker needs to reason about. We evaluate the approach with experiments in several domains and compare against other state-of-the-art methods. Experiments are done in a collaborative multi-agent and a single agent object manipulation domain. The experiments are performed both in simulation and on a real robot. The framework handles reasoning with uncertain agent behaviors and with unknown object and environment parametrizations well. The results show good performance and indicate that the proposed approach can improve existing state-of-the-art methods.

    更新日期:2020-01-04
  • Learning in the Machine: Random Backpropagation and the Deep Learning Channel.
    Artif. Intell. (IF 4.483) Pub Date : 2018-05-08
    Pierre Baldi,Peter Sadowski,Zhiqin Lu

    Random backpropagation (RBP) is a variant of the backpropagation algorithm for training neural networks, where the transpose of the forward matrices are replaced by fixed random matrices in the calculation of the weight updates. It is remarkable both because of its effectiveness, in spite of using random matrices to communicate error information, and because it completely removes the taxing requirement of maintaining symmetric weights in a physical neural system. To better understand random backpropagation, we first connect it to the notions of local learning and learning channels. Through this connection, we derive several alternatives to RBP, including skipped RBP (SRPB), adaptive RBP (ARBP), sparse RBP, and their combinations (e.g. ASRBP) and analyze their computational complexity. We then study their behavior through simulations using the MNIST and CIFAR-10 bechnmark datasets. These simulations show that most of these variants work robustly, almost as well as backpropagation, and that multiplication by the derivatives of the activation functions is important. As a follow-up, we study also the low-end of the number of bits required to communicate error information over the learning channel. We then provide partial intuitive explanations for some of the remarkable properties of RBP and its variations. Finally, we prove several mathematical results, including the convergence to fixed points of linear chains of arbitrary length, the convergence to fixed points of linear autoencoders with decorrelated data, the long-term existence of solutions for linear systems with a single hidden layer and convergence in special cases, and the convergence to fixed points of non-linear chains, when the derivative of the activation functions is included.

    更新日期:2019-11-01
  • The Local Geometry of Multiattribute Tradeoff Preferences.
    Artif. Intell. (IF 4.483) Pub Date : 2011-04-30
    Michael McGeachie,Jon Doyle

    Existing representations for multiattribute ceteris paribus preference statements have provided useful treatments and clear semantics for qualitative comparisons, but have not provided similarly clear representations or semantics for comparisons involving quantitative tradeoffs. We use directional derivatives and other concepts from elementary differential geometry to interpret conditional multiattribute ceteris paribus preference comparisons that state bounds on quantitative tradeoff ratios. This semantics extends the familiar economic notion of marginal rate of substitution to multiple continuous or discrete attributes. The same geometric concepts also provide means for interpreting statements about the relative importance of different attributes.

    更新日期:2019-11-01
  • Updating action domain descriptions.
    Artif. Intell. (IF 4.483) Pub Date : 2010-12-15
    Thomas Eiter,Esra Erdem,Michael Fink,Ján Senko

    Incorporating new information into a knowledge base is an important problem which has been widely investigated. In this paper, we study this problem in a formal framework for reasoning about actions and change. In this framework, action domains are described in an action language whose semantics is based on the notion of causality. Unlike the formalisms considered in the related work, this language allows straightforward representation of non-deterministic effects and indirect effects of (possibly concurrent) actions, as well as state constraints; therefore, the updates can be more general than elementary statements. The expressivity of this formalism allows us to study the update of an action domain description with a more general approach compared to related work. First of all, we consider the update of an action description with respect to further criteria, for instance, by ensuring that the updated description entails some observations, assertions, or general domain properties that constitute further constraints that are not expressible in an action description in general. Moreover, our framework allows us to discriminate amongst alternative updates of action domain descriptions and to single out a most preferable one, based on a given preference relation possibly dependent on the specified criteria. We study semantic and computational aspects of the update problem, and establish basic properties of updates as well as a decomposition theorem that gives rise to a divide and conquer approach to updating action descriptions under certain conditions. Furthermore, we study the computational complexity of decision problems around computing solutions, both for the generic setting and for two particular preference relations, viz. set-inclusion and weight-based preference. While deciding the existence of solutions and recognizing solutions are PSPACE-complete problems in general, the problems fall back into the polynomial hierarchy under restrictions on the additional constraints. We finally discuss methods to compute solutions and approximate solutions (which disregard preference). Our results provide a semantic and computational basis for developing systems that incorporate new information into action domain descriptions in an action language, in the presence of additional constraints.

    更新日期:2019-11-01
  • A comparative runtime analysis of heuristic algorithms for satisfiability problems.
    Artif. Intell. (IF 4.483) Pub Date : 2010-02-04
    Yuren Zhou,Jun He,Qing Nie

    The satisfiability problem is a basic core NP-complete problem. In recent years, a lot of heuristic algorithms have been developed to solve this problem, and many experiments have evaluated and compared the performance of different heuristic algorithms. However, rigorous theoretical analysis and comparison are rare. This paper analyzes and compares the expected runtime of three basic heuristic algorithms: RandomWalk, (1+1) EA, and hybrid algorithm. The runtime analysis of these heuristic algorithms on two 2-SAT instances shows that the expected runtime of these heuristic algorithms can be exponential time or polynomial time. Furthermore, these heuristic algorithms have their own advantages and disadvantages in solving different SAT instances. It also demonstrates that the expected runtime upper bound of RandomWalk on arbitrary k-SAT(k >/= 3) is O((k - 1)(n)), and presents a k-SAT instance that has Theta((k - 1)(n)) expected runtime bound.

    更新日期:2019-11-01
  • The Dropout Learning Algorithm.
    Artif. Intell. (IF 4.483) Pub Date : 2014-04-29
    Pierre Baldi,Peter Sadowski

    Dropout is a recently introduced algorithm for training neural network by randomly dropping units during training to prevent their co-adaptation. A mathematical analysis of some of the static and dynamic properties of dropout is provided using Bernoulli gating variables, general enough to accommodate dropout on units or connections, and with variable rates. The framework allows a complete analysis of the ensemble averaging properties of dropout in linear networks, which is useful to understand the non-linear case. The ensemble averaging properties of dropout in non-linear logistic networks result from three fundamental equations: (1) the approximation of the expectations of logistic functions by normalized geometric means, for which bounds and estimates are derived; (2) the algebraic equality between normalized geometric means of logistic functions with the logistic of the means, which mathematically characterizes logistic functions; and (3) the linearity of the means with respect to sums, as well as products of independent variables. The results are also extended to other classes of transfer functions, including rectified linear functions. Approximation errors tend to cancel each other and do not accumulate. Dropout can also be connected to stochastic neurons and used to predict firing rates, and to backpropagation by viewing the backward propagation as ensemble averaging in a dropout linear network. Moreover, the convergence properties of dropout can be understood in terms of stochastic gradient descent. Finally, for the regularization properties of dropout, the expectation of the dropout gradient is the gradient of the corresponding approximation ensemble, regularized by an adaptive weight decay term with a propensity for self-consistent variance minimization and sparse representations.

    更新日期:2019-11-01
  • Using Wikipedia to learn semantic feature representations of concrete concepts in neuroimaging experiments.
    Artif. Intell. (IF 4.483) Pub Date : 2012-12-18
    Francisco Pereira,Matthew Botvinick,Greg Detre

    In this paper we show that a corpus of a few thousand Wikipedia articles about concrete or visualizable concepts can be used to produce a low-dimensional semantic feature representation of those concepts. The purpose of such a representation is to serve as a model of the mental context of a subject during functional magnetic resonance imaging (fMRI) experiments. A recent study [19] showed that it was possible to predict fMRI data acquired while subjects thought about a concrete concept, given a representation of those concepts in terms of semantic features obtained with human supervision. We use topic models on our corpus to learn semantic features from text in an unsupervised manner, and show that those features can outperform those in [19] in demanding 12-way and 60-way classification tasks. We also show that these features can be used to uncover similarity relations in brain activation for different concepts which parallel those relations in behavioral data from human subjects.

    更新日期:2019-11-01
  • Methods for solving reasoning problems in abstract argumentation - A survey.
    Artif. Intell. (IF 4.483) Pub Date : 2015-03-05
    Günther Charwat,Wolfgang Dvořák,Sarah A Gaggl,Johannes P Wallner,Stefan Woltran

    Within the last decade, abstract argumentation has emerged as a central field in Artificial Intelligence. Besides providing a core formalism for many advanced argumentation systems, abstract argumentation has also served to capture several non-monotonic logics and other AI related principles. Although the idea of abstract argumentation is appealingly simple, several reasoning problems in this formalism exhibit high computational complexity. This calls for advanced techniques when it comes to implementation issues, a challenge which has been recently faced from different angles. In this survey, we give an overview on different methods for solving reasoning problems in abstract argumentation and compare their particular features. Moreover, we highlight available state-of-the-art systems for abstract argumentation, which put these methods to practice.

    更新日期:2019-11-01
  • Modeling the Complex Dynamics and Changing Correlations of Epileptic Events.
    Artif. Intell. (IF 4.483) Pub Date : 2014-10-07
    Drausin F Wulsin,Emily B Fox,Brian Litt

    Patients with epilepsy can manifest short, sub-clinical epileptic "bursts" in addition to full-blown clinical seizures. We believe the relationship between these two classes of events-something not previously studied quantitatively-could yield important insights into the nature and intrinsic dynamics of seizures. A goal of our work is to parse these complex epileptic events into distinct dynamic regimes. A challenge posed by the intracranial EEG (iEEG) data we study is the fact that the number and placement of electrodes can vary between patients. We develop a Bayesian nonparametric Markov switching process that allows for (i) shared dynamic regimes between a variable number of channels, (ii) asynchronous regime-switching, and (iii) an unknown dictionary of dynamic regimes. We encode a sparse and changing set of dependencies between the channels using a Markov-switching Gaussian graphical model for the innovations process driving the channel dynamics and demonstrate the importance of this model in parsing and out-of-sample predictions of iEEG data. We show that our model produces intuitive state assignments that can help automate clinical analysis of seizures and enable the comparison of sub-clinical bursts and full clinical seizures.

    更新日期:2019-11-01
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