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A study is conducted to examine the trading agents competing with the help of data from actual TAC tournaments and some post competition experimentation. This helps to quantify TAC travel market effectiveness in terms of allocative efficiency and identify opportunities for further gains from trade.

Probabilistic context-free grammars (PCFGs) provide a simple way to represent a particular class of distributions over sentences in a context-free language. Efficient parsing algorithms for answering particular queries about a PCFG (i.e., calculating the probability of a given sentence, or finding the most likely parse) have been developed and applied to a variety of pattern-recognition problems. We extend the class of queries that can be answered in several ways: (1) allowing missing tokens in a sentence or sentence fragment, (2) supporting queries about intermediate structure, such as the presence of particular nonterminals, and (3) flexible conditioning on a variety of types of evidence. Our method works by constructing a Bayesian network to represent the distribution of parse trees induced by a given PCFG. The network structure mirrors that of the chart in a standard parser, and is generated using a similar dynamic-programming approach. We present an algorithm for constructing Bayesian networks from PCFGs, and show how queries or patterns of queries on the network correspond to interesting queries on PCFGs. The network formalism also supports extensions to encode various context sensitivities within the probabilistic dependency structure. Index Terms - Probabilistic context-free grammars, Bayesian networks.

Artificial intelligence (AI) offers applications in extracting specific information from large multimedia digital collections, thus improving advanced document-processing capabilities. AI assists in developing information agents that lessen the burdens of users in searching, filtering, and retrieving information. AI can contribute in the area of language and protocol design for agent communication regarding the development of digital libraries.

Explaining away is a common pattern of reasoning in which the confirmation of one cause of an observed or believed event reduces the need to invoke alternative causes. The opposite of explaining away also can occur, where the confirmation of one cause increases belief in another. We provide a general qualitative probabilistic analysis of intercausal reasoning and identify the property of the interaction among the causes (product synergy) that determines which form of reasoning is appropriate. Product synergy extends the qualitative probabilistic network (QPN) formalism to support qualitative intercausal inference about the directions of change in probabilistic belief. The intercausal relation also justifies Occam's razor, facilitating pruning in the search for likely diagnoses.

We study the problem of allocating limited security countermeasures to protect network data from cyber-attacks, for scenarios modeled by Bayesian attack graphs. We consider multistage interactions between a network administrator and cybercriminals, formulated as a security game. This formulation is capable of representing security environments with significant dynamics and uncertainty and very large strategy spaces. We propose parameterized heuristic strategies for the attacker and defender and provide detailed analysis of their time complexity. Our heuristics exploit the topological structure of attack graphs and employ sampling methods to overcome the computational complexity in predicting opponent actions. Due to the complexity of the game, we employ a simulation-based approach and perform empirical game analysis over an enumerated set of heuristic strategies. Finally, we conduct experiments in various game settings to evaluate the performance of our heuristics in defending networks, in a manner that is robust to uncertainty about the security environment. [ABSTRACT FROM AUTHOR]

Credit networks are an abstraction for modeling trust among agents in a network. Agents who do not directly trust each other can transact through exchange of IOUs (obligations) along a chain of trust in the network. Credit networks are robust to intrusion, can enable transactions between strangers in exchange economies, and have the liquidity to support a high rate of transactions. We study the formation of such networks when agents strategically decide how much credit to extend each other. We find strong positive network formation results for the simplest theoretical model. When each agent trusts a fixed set of other agents and transacts directly only with those it trusts, all pure-strategy Nash equilibria are social optima. However, when we allow transactions over longer paths, the price of anarchy may be unbounded. On the positive side, when agents have a shared belief about the trustworthiness of each agent, simple greedy dynamics quickly converge to a star-shaped network, which is a social optimum. Similar star-like structures are found in equilibria of heuristic strategies found via simulation studies. In addition, we simulate environments where agents may have varying information about each others' trustworthiness based on their distance in a social network. Empirical game analysis of these scenarios suggests that star structures arise only when defaults are relatively rare, and otherwise, credit tends to be issued over short social distances conforming to the locality of information. Overall, we find that networks formed by self-interested agents achieve a high fraction of available value, as long as this potential value is large enough to enable any network to form. [ABSTRACT FROM AUTHOR]

Autonomous agents should be market aware in the sense that they should be adept in interacting with and through software market institutions. The argument for market awareness is based on the premise that resource allocation play an important part in the design of multiagent systems and that price systems provide structure to the allocation problem. The rationale and implications for the market-based approach affect various issues on the design of computational markets.

Research in privacy-preserving data publishing has revealed the necessity of accounting for an adversary's background knowledge when reasoning about the protection afforded by various anonymization schemes. Most existing work models the background knowledge of one individual adversary or privacy attacker, or makes a worst-case assumption that attackers will act as one: colluding through sharing of background information. We propose a framework for modeling multiple attackers with heterogeneous background knowledge, supporting analysis of their strategic incentives for sharing information prior to attack. The framework posits a decentralized mechanism by which agents decide whether and how much information to share, and defines a normal-form game representing their strategic choice setting. Through a simple example, we show that the efficacy of database generalization operations depends on the information-sharing strategies adopted by the attackers. Through analysis of the underlying game model, a database publisher can adopt a generalization level geared to the level of sharing expected among rational attackers. [ABSTRACT FROM AUTHOR]

Recent advances in enterprise web-based software have created a need for sophisticated yet user-friendly data-quality solutions. A new category of data-quality solutions that fill this need using intelligent matching and retrieval algorithms is discussed. Solutions are focused on customer and sales data and include real-time inexact search, batch processing, and data migration. Users are empowered to maintain higher quality data resulting in more efficient sales and marketing operations. Sales managers spend more time with customers and less time managing data. [ABSTRACT FROM AUTHOR]

We develop multiattribute auctions that accommodate generalized additive independent (GAI) preferences. We propose an iterative auction mechanism that maintains prices on potentially overlapping GAI clusters of attributes, thus decreases elicitation and computational burden, and creates an open competition among suppliers over a multidimensional domain. Most significantly, the auction is guaranteed to achieve surplus which approximates optimal welfare up to a small additive factor, under reasonable equilibrium strategies of traders. The main departure of GAI auctions from previous literature is to accommodate non-additive trader preferences, hence allowing traders to condition their evaluation of specific attributes on the value of other attributes. At the same time, the GAI structure supports a compact representation of prices, enabling a tractable auction process. We perform a simulation study, demonstrating and quantifying the significant efficiency advantage of more expressive preference modeling. We draw random GAI-structured utility functions with various internal structures, generate additive functions that approximate the GAI utility, and compare the performance of the auctions using the two representations. We find that allowing traders to express existing dependencies among attributes improves the economic efficiency of multiattribute auctions. [ABSTRACT FROM AUTHOR]

We introduce CUI networks, a compact graphical representation of utility functions over multiple attributes. CUI networks model multiattribute utility functions using the well-studied and widely applicable utility independence concept. We show how conditional utility independence leads to an effective functional decomposition that can be exhibited graphically, and how local, compact data at the graph nodes can be used to calculate joint utility. We discuss aspects of elicitation, network construction, and optimization, and contrast our new representation with previous graphical preference modeling. [ABSTRACT FROM AUTHOR]

We study multiunit double auctions accepting bids with indivisibility constraints. Modeling the auction problem as a Multiple Choice Knapsack Problem and using dynamic programming, we show that incremental computations during bid processing can speed the handling of key auction operations such as clearing and quoting. We propose different price-quote policies and study their influence on the efficiency of market-based allocation. Using a reconfigurable manufacturing scenario where agents trade large quantities of multiple goods, we demonstrate potential benefits of supporting indivisibility constraints in bidding. These benefits are highly sensitive to the form of price quote provided, indicating interesting tradeoffs in communication and allocation efficiency. [ABSTRACT FROM AUTHOR]

The TAC 2003 supply-chain game presented automated trading agents with a challenging strategic problem. Embedded within a high-dimensional stochastic environment was a pivotal strategic decision about initial procurement of components. Early evidence suggested that the entrant field was headed toward a self-destructive, mutually unprofitable equilibrium. Our agent, Deep Maize, introduced a preemptive strategy designed to neutralize aggressive procurement, perturbing the field to a more profitable equilibrium; it worked. Not only did preemption improve Deep Maize's profitability, it improved profitability for the whole field. Whereas it is perhaps counterintuitive that action designed to prevent others from achieving their goals actually helps them, strategic analysis employing an empirical game-theoretic methodology verifies and provides insight about this outcome. [ABSTRACT FROM AUTHOR]

We extend Q-learning to a noncooperative multiagent context, using the framework of generalsum stochastic games. A learning agent maintains Q-functions over joint actions, and performs updates based on assuming Nash equilibrium behavior over the current Q-values. This learning protocol provably converges given certain restrictions on the stage games (defined by Q-values) that arise during learning. Experiments with a pair of two-player grid games suggest that such restrictions on the game structure are not necessarily required. Stage games encountered during learning in both grid environments violate the conditions. However, learning consistently converges in the first grid game, which has a unique equilibrium Q-function, but sometimes fails to converge in the second, which has three different equilibrium Q-functions. In a comparison of offline learning performance in both games, we find agents are more likely to reach a joint optimal path with Nash Q-learning than with a single-agent Q-learning method. When at least one agent adopts Nash Q-learning, the performance of both agents is better than using single-agent Q-learning. We have also implemented an online version of Nash Q-learning that balances exploration with exploitation, yielding improved performance. [ABSTRACT FROM AUTHOR]

The 2002 Trading Agent Competition (TAC) presented a challenging market game in the domain of travel shopping. One of the pivotal issues in this domain is uncertainty about hotel prices, which have a significant influence on the relative cost of alternative trip schedules. Thus, virtually all participants employ some method for predicting hotel prices. We survey approaches employed in the tournament, finding that agents apply an interesting diversity of techniques, taking into account differing sources of evidence bearing on prices. Based on data provided by entrants on their agents' actual predictions in the TAC-02 finals and semifinals, we analyze the relative efficacy of these approaches. The results show that taking into account game-specific information about flight prices is a major distinguishing factor. Machine learning methods effectively induce the relationship between flight and hotel prices from game data, and a purely analytical approach based on competitive equilibrium analysis achieves equal accuracy with no historical data. Employing a new measure of prediction quality, we relate absolute accuracy to bottom-line performance in the game. [ABSTRACT FROM AUTHOR]

Examines the specifications for electronic auction mechanisms. Identification of several core activities that structure the design space; Function of formal parameters; Use of an auction description language.

In the course of playing or solving a game, it is common to face a series of changing otheragent strategies. These strategies often share elements: the set of possible policies to play has overlap, and the policies are sampled at the beginning of play by possibly differing distributions. As it faces the series of strategies, therefore, an agent has the opportunity to transfer its learned play against the previously encountered other-agent policies. We tackle two problems: (1) how can learned responses transfer across changing opponent strategies, and (2) how can this transfer be used to reduced the cumulative cost of learning in game solving. The first problem we characterize as the strategic knowledge transfer problem. For value-based response policies, we demonstrate that Q-Mixing approximately solves this problem by appropriately averaging the component Q-values. Solutions to the first problem can be applied to reduce the computational cost of learning-based game solving algorithms. We offer two algorithms that operate within the Policy-Space Response Oracles (PSRO) framework. Mixed-Oracles reduces the per-policy construction cost by transferring responses from previously encountered opponents. Mixed-Opponents performs strategic knowledge transfer by combining the previously encountered opponents into a single novel policy. Experimental evaluation of these methods on general-sum grid-world games provide evidence about their advantages and limitations in comparison to standard PSRO. [ABSTRACT FROM AUTHOR]

We present an agent-based model of manipulating prices in financial markets through spoofing: submitting spurious orders to mislead traders who learn from the order book. Our model captures a complex market environment for a single security, whose common value is given by a dynamic fundamental time series. Agents trade through a limit-order book, based on their private values and noisy observations of the fundamental. We consider background agents following two types of trading strategies: the non-spoofable zero intelligence (ZI) that ignores the order book and the manipulable heuristic belief learning (HBL) that exploits the order book to predict price outcomes. We conduct empirical game-theoretic analysis upon simulated agent payoffs across parametrically different environments and measure the effect of spoofing on market performance in approximate strategic equilibria. We demonstrate that HBL traders can benefit price discovery and social welfare, but their existence in equilibrium renders a market vulnerable to manipulation: simple spoofing strategies can effectively mislead traders, distort prices and reduce total surplus. Based on this model, we propose to mitigate spoofing from two aspects: (1) mechanism design to disincentivize manipulation; and (2) trading strategy variations to improve the robustness of learning from market information. We evaluate the proposed approaches, taking into account potential strategic responses of agents, and characterize the conditions under which these approaches may deter manipulation and benefit market welfare. Our model provides a way to quantify the effect of spoofing on trading behavior and market efficiency, and thus it can help to evaluate the effectiveness of various market designs and trading strategies in mitigating an important form of market manipulation. [ABSTRACT FROM AUTHOR]

Traces the events that lead to the success of the `Journal of Artificial Intelligence Research' (JAIR) periodical. History of the journal; Active participation in dissemination of academic and scientific information through the Internet; Publication systems and procedures; Guiding philosophy; Electronic publications; Prospects for the coming years.