In an agent-based online auction system, a bidding agent can automatically place bids on behalf of a human user according to a user-specified bidding strategy. Current implementations of bidding agents only support a set of simple predefined bidding strategies. In this paper, we introduce a formal bidding strategy model that supports specification of complex bidding strategies for autonomous bidding agents. The formal model is defined as a layered bidding strategy model (LBSM), which can be represented using notations adapted from UML activity diagrams. For real-time and efficient reasoning, the formal model is converted into a rule-based bidding strategy model (RBSM) represented in bidding strategy language (BSL), which can be directly executed by a reasoning module of an autonomous bidding agent. We present an algorithm for converting an LBSM to a rule-based bidding strategy model, and an algorithm to drive the reasoning engine. Finally, we develop a prototype agent-based online auction system using JADE, and demonstrate how layered bidding strategies can be precisely specified, and how our approach may support analysis of impacts on bidding histories by using different bidding strategies in agent-based online auctions.

Current implementations of agent-based online auction systems only support simple predefined bidding strategies for bidding agents. In this paper, we introduce a formal bidding strategy model for specification of flexible and complex bidding strategies. The formal model is defined as a layered bidding strategy model (LBSM), which can be represented using notations borrowed from UML activity diagrams. To support real-time and efficient reasoning, the formal model is converted into a rule-based bidding strategy model (RBSM) specified in bidding strategy language (BSL) that can be directly executed by a reasoning module of a bidding agent. We present an algorithm for converting an LBSM to an RBSM, and an algorithm to drive the reasoning engine. Finally, we develop a prototype agent-based online auction system using JADE, and illustrate how flexible and complex bidding strategies can be precisely specified and efficiently executed.

Online auctions are vulnerable to shill bidders, who intend to artificially raise bidding prices, causing winning bidders to pay more than they should pay for auctioned items. Detection of such fraudulent behaviors is very difficult, especially when an auction is in progress, or “live”. This paper focuses on a formal technique to detect shilling behaviors in live online auctions. We define a monitoring agent that can continuously watch for abnormal bidding behaviors of a monitored bidder. To make the detection process efficient, we introduce a dynamic auction model (DAM), and use real-time model checking techniques to verify shilling behaviors specified in linear temporal logic (LTL). Finally, we present an algorithm for real-time shill detection, and use a case study to demonstrate the efficiency and effectiveness of our approach.

Current electronic commerce applications such as online auction systems are not trustworthy due to a lack of effective trust management mechanisms. A trustworthy online auction system requires a dynamic trust management module that can detect abnormal bidding activities in real-time, notify the involved users, and cancel the corresponding auction immediately. In this paper, we present a general framework for agent-based trust management (ATM) in online auctions. The ATM module consists of three types of agents, namely the monitoring agent, the analysis agent and the security agent. A monitoring agent can monitor a bidder and detect any abnormal bidding behavior; while the analysis agent and the security agent can analyze state-based information and history information of a bidder, and make decisions on skill detection, respectively. We illustrate the communication protocol among various agents, and demonstrate our agent-based trust management approach for online auctions using a prototype ATM module developed with JADE.