606 research outputs found
Flexible Decision Control in an Autonomous Trading Agent
An autonomous trading agent is a complex piece of software that must operate in a competitive economic environment and support a research agenda. We describe the structure of decision processes in the MinneTAC trading agent, focusing on the use of evaluators – configurable, composable modules for data analysis and prediction that are chained together at runtime to support agent decision-making. Through a set of examples, we show how this structure supports sales and procurement decisions, and how those decision processes can be modified in useful ways by changing evaluator configurations. To put this work in context, we also report on results of an informal survey of agent design approaches among the competitors in the Trading Agent Competition for Supply Chain Management (TAC SCM)
Price Prediction in a Trading Agent Competition
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
Use of Markov Chains to Design an Agent Bidding Strategy for Continuous Double Auctions
As computational agents are developed for increasingly complicated e-commerce
applications, the complexity of the decisions they face demands advances in
artificial intelligence techniques. For example, an agent representing a seller
in an auction should try to maximize the seller's profit by reasoning about a
variety of possibly uncertain pieces of information, such as the maximum prices
various buyers might be willing to pay, the possible prices being offered by
competing sellers, the rules by which the auction operates, the dynamic arrival
and matching of offers to buy and sell, and so on. A naive application of
multiagent reasoning techniques would require the seller's agent to explicitly
model all of the other agents through an extended time horizon, rendering the
problem intractable for many realistically-sized problems. We have instead
devised a new strategy that an agent can use to determine its bid price based
on a more tractable Markov chain model of the auction process. We have
experimentally identified the conditions under which our new strategy works
well, as well as how well it works in comparison to the optimal performance the
agent could have achieved had it known the future. Our results show that our
new strategy in general performs well, outperforming other tractable heuristic
strategies in a majority of experiments, and is particularly effective in a
'seller?s market', where many buy offers are available
Towards autonomous decision-making: A probabilistic model for learning multi-user preferences
Information systems have revolutionized the provisioning of decision-relevant information, and decision support tools have improved human decisions in many domains. Autonomous decision- making, on the other hand, remains hampered by systems’ inability to faithfully capture human preferences. We present a computational preference model that learns unobtrusively from lim- ited data by pooling observations across like-minded users. Our model quantifies the certainty of its own predictions as input to autonomous decision-making tasks, and it infers probabilistic segments based on user choices in the process. We evaluate our model on real-world preference data collected on a commercial crowdsourcing platform, and we find that it outperforms both individual and population-level estimates in terms of predictive accuracy and the informative- ness of its certainty estimates. Our work takes an important step toward systems that act autonomously on their users’ behalf
Approximate strategic reasoning through hierarchical reduction of large symmetric games
To deal with exponential growth in the size of a game with the number of agents, we propose an approximation based on a hierarchy of reduced games. The reduced game achieves sav-ings by restricting the number of agents playing any strategy to fixed multiples. We validate the idea through experiments on randomly generated local-effect games. An extended ap-plication to strategic reasoning about a complex trading sce-nario motivates the approach, and demonstrates methods for game-theoretic reasoning over incompletely-specified games at multiple levels of granularity
Automated Markets and Trading Agents
Computer automation has the potential, just starting to be realized, of transforming the
design and operation of markets, and the behaviors of agents trading in them. We discuss
the possibilities for automating markets, presenting a broad conceptual framework
covering resource allocation as well as enabling marketplace services such as search
and transaction execution. One of the most intriguing opportunities is provided by markets
implementing computationally sophisticated negotiation mechanisms, for example
combinatorial auctions. An important theme that emerges from the literature is the centrality
of design decisions about matching the domain of goods over which a mechanism
operates to the domain over which agents have preferences. When the match is imperfect
(as is almost inevitable), the market game induced by the mechanism is analytically
intractable, and the literature provides an incomplete characterization of rational bidding
policies. A review of the literature suggests that much of our existing knowledge
comes from computational simulations, including controlled studies of abstract market
designs (e.g., simultaneous ascending auctions), and research tournaments comparing
agent strategies in a variety of market scenarios. An empirical game-theoretic methodology
combines the advantages of simulation, agent-based modeling, and statistical and
game-theoretic analysis.http://deepblue.lib.umich.edu/bitstream/2027.42/49510/1/ace_galleys.pd
Unsolved Problems in ML Safety
Machine learning (ML) systems are rapidly increasing in size, are acquiring
new capabilities, and are increasingly deployed in high-stakes settings. As
with other powerful technologies, safety for ML should be a leading research
priority. In response to emerging safety challenges in ML, such as those
introduced by recent large-scale models, we provide a new roadmap for ML Safety
and refine the technical problems that the field needs to address. We present
four problems ready for research, namely withstanding hazards ("Robustness"),
identifying hazards ("Monitoring"), reducing inherent model hazards
("Alignment"), and reducing systemic hazards ("Systemic Safety"). Throughout,
we clarify each problem's motivation and provide concrete research directions.Comment: Position Pape
E-Negotiation systems: A theoretical framework and empirical investigation
Ph.DDOCTOR OF PHILOSOPH
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