Adaptive Decision Support for Academic Course Scheduling Using Intelligent Software Agents

Academic course scheduling is a complex operation that requires the interaction between different users including instructors and course schedulers to satisfy conflicting constraints in an optimal manner. Traditionally, this problem has been addressed as a constraint satisfaction problem where the constraints are stationary over time. In this paper, we address academic course scheduling as a dynamic decision support problem using an agent-enabled adaptive decision support system. In this paper, we describe the Intelligent Agent Enabled Decision Support (IAEDS) system, which employs software agents to assist humans in making strategic decisions under dynamic and uncertain conditions. The IAEDS system has a layered architecture including different components such as a learning engine that uses historic data to improve decision-making and an intelligent applet base that provides graphical interface templates to users for frequently requested decision-making tasks. We illustrate an application of our IAEDS system where agents are used to make complex scheduling decisions in a dynamically changing environment

A Multi-Agent Prediction Market Based on Boolean Network Evolution

—Prediction markets have been shown to be a useful tool in forecasting the outcome of future events by aggregating public opinion about the events’ outcome. Previous research on prediction markets has mostly analyzed the prediction markets by building complex analytical models. In this paper, we posit that simpler yet powerful Boolean rules can be used to adequately describe the operations of a prediction market. We have used a multi-agent based prediction market where Boolean network based rules are used to capture the evolution of the beliefs of the market’s participants, as well as to aggregate the prices in the market. We show that despite the simplification of the traders’ beliefs in the prediction market into Boolean states, the aggregated market price calculated using our BN model is strongly correlated with the price calculated by a commonly used aggregation strategy in existing prediction markets called the Logarithmic Market Scoring Rule (LMSR). We also empirically show that our Boolean network-based prediction market can stabilize market prices under the presence of untruthful belief revelation by the traders

Heuristics for Uninformed Search Algorithms in Unstructured P2P Networks Inspired by Self- Organizing Social Insect Models

Abstract: We consider the problem of rapidly searching for resources or files in a distributed, unstructured, peer-to-peer file sharing network. Unstructured p2p network protocols such as Gnutella use a flooding-based mechanism for resource searching that generates considerable traffic in the network for each search query. When the searching activity by users in a p2p network is high, the traffic generated from the search requests could ensue congestion and result in increased search latency and poor performance in the entire network. To address this problem, we describe a resource search algorithm for p2p networks inspired by the stigmergetic behavior of ants while searching for food. Ants are used to encapsulate a search query initiated by a user in the p2p network. To search for the resource corresponding to their search query among the nodes of the network, each ant associates a certain amount of virtual pheromone with the nodes it visits. Later on, ants searching for resources use the amount and type of pheromone associated by previous ants with each node along their search path to direct the search query towards nodes that have a higher probability of resulting in the success for the search. We have tested our algorithm extensively within a simulated p2p network. Our simulation results show that our ant-based heuristics perform better than a completely uninformed or blind search that requires similar message overhead for each search query. When compared to a flooding-based mechanism, although the ant based search heuristic performs less efficiently under certain circumstances, it is capable of reducing the message overhead per search query by an exponential amount with respect to the flooding-based mechanism

A Peer-to-Peer System Architecture for Multi-Agent Collaboration

A peer-to-peer(P2P) network comprises a collection of nodes that can cooperate and collaborate with each other in a de-centralized and distributed manner. A node in a P2P network can access information present in the network using peer discovery followed by a search and retrieval phase. At present, most P2P systems employ message communication to implement the operations in a P2P network. In this paper, we propose the use of mobile software agents to implement the protocols in a P2P system. Mobile software agents are autonomous, economic in terms of size and bandwidth consumption, and can operate remotely without the continuous supervision of a central server. Our research indicates that mobile software agents provide a suitable paradigm for implementing P2P systems that is both scalable and robust. 1