Abstract: In a swarm weapon system, groups of mobile robotic vehicles (MRVs) are organized into a leader-follower configuration in which follower drone MRVs supply sensor data to a lead MRV. The lead MRV analyzes data and makes decisions about how the collective of automated robots will behave as a group. The hierarchical control model will centralize processes in order for varied spatial configurations of the network to be made in real time.

Abstract: Unmanned underwater vehicles (UUVs) are described for use in a swarm weapon system. UUVs use distinctive patterns of communication and organization in order to configure into specific formations for an attack on a specific targets. By receiving sensor data from UUV drones, a lead UUV will make decisions on how to reorganize the grouping according to specific mission parameters. In this way, the UUVs interact with the environment and reorganize into reconfigurations in order to complete a mission. There are numerous applications of squads of UUVs, from submarine, torpedo, mine and diver identification and attack to sentry duty organization.

Abstract: In a swarm weapon system, decision-making processes are described for the organization of mobile robotic vehicles (MRVs). After MRV drone sensor data is provided to a lead MRV, an initial decision is made by the lead MRV as a result of the selection of a winning simulation that provides the best opportunity for success of a mission. Once this decision is made, actions are organized for the MRVs, which provide feedback for the continuation of the process until the mission is completed.

Abstract: A method is disclosed for the exchange of information between a buyer intelligent inter-agent and at least two seller intelligent inter-agents in a distributed commercial system. Collaboration processes facilitate multilateral requests for products, services and bundles that are made-to-order in a disintermediated commercial system. Collaboration allows multilateral multivariate customization in automated commerce. Information can be analyzed by AAs for interpretation by buyer or seller. Information collaboration can be concurrent with or prior to a CSA first search query and precedes automated transaction negotiation.

Abstract: A commercial search agent (CSA) is used to access data in a distributed communications system. An initial search request constitutes a buyer's first search query at sellers' commercial database. Analytical agents and seller promotions are used to inform the CSA, while negotiation commences after the initial query.

Abstract: Cooperative intelligent negotiation agents (C-INAs) operate in a distributed communications environment for the automated negotiation and procurement of products, services and bundles. Buyer-side aggregation occurs by pooling C-INAs with common interests within specific time frames that are activated by seller INA promotions. Buyer C-INAs can have several modes, including neutral agency (broker or intermediary) role, the leader role and the follower role. C-INAs use multilateral and multivariate negotiation parameters. Sellers (using S-C-INAs) may cooperate in order to share information so as to provide correct pricing for complex combinations of items from buyers. Buyers can cooperate in order to receive aggregated pricing opportunities on multiple item bundles from a plurality of sellers using combinatorial auctions. Dynamic INAs, which switch buyer and seller role, may also use aggregation and combinatorial auction methods.

Abstract: A distributed computer network system is described for commercial applications. A “co-op” is formed within a specific industry using common registries and languages to form a matrix of showcases for the sale of products, services and bundles. Data is continuously updated and cleansed by the showcase inter-agent. Commercial Search Agents (CSAs) are used to access data and fulfill queries. Data mining is possible via analytical agents (AAs) by assessing patterns among data in the system and building profiles. Promotions, including dynamic pricing opportunities, can be integrated into the data stream.

Abstract: In a distributed communications environment, intelligent negotiation agents (INAS) are described. INAs are autonomous intelligent software agents that negotiate for the acquisition of products, services and bundles by adopting roles of buying, selling and brokering in which a buyer agent negotiates with at least two seller agents. In order to automate INAs, artificial intelligence technologies, including neural networks, genetic algorithms and genetic programming, are applied. AI allows automous software agents to adapt to changing markets and allows the INAs to use mobility in a distributed system. Multi-session auction approaches based on initial parameters and self-motivated adaptation are used by buyer and seller INAs. Negotiation factors include multilateral and multivariate parameters as well as price. Accountability data, marketing promotions, risk management options and made-to-order services are integrated into this system. Search agents initiate the negotiation process.