An Environmental Multiagent Architecture for Health Management

1. An Environmental Multiagent Architecturefor Health Management Francesco Amigoni Nicola Gatti

2. Summary • We propose a multiagent architectural solution, called environmental agency, for the flexible cooperation and coordination of the devices pervading an environment in order to satisfy the needs of human beings, in particular of impaired people

3. Application Scenario • Pervasive, intelligent, and interacting devices for human beings health monitoring

4. Anthropic Agency • We implemented a simulated multiagent system, called anthropic agency, for the regulation of the glucose-insulin metabolism in simulated diabetic patients [Amigoni, Dini, Gatti, Somalvico, 2003] • The user interface module, called anthropic majordomo, is implemented on a palmtop computer • There are situations (exigencies) that the anthropic agency cannot manage by itself: • the insulin tank is empty • an hardware component is malfunctioning • the biological state of the patient is alarming • a telemedicine consult is required

5. Environmental Agency • An environmental agency is a cooperative multiagent system whose agents are pervasively distributed in an environment • For our purposes, an environment can be a room, an apartment, a building, ... • Note that an environment (such as an apartment) can be composed of several sub-environments (such as the rooms)

6. Environmental Agency Architecture • In order to cope with the device heterogeneity we arranged each agent in two parts: the cooperative semiagent (CO) and the operative semiagent (OP) • The agents of an environmental agency are usually heterogeneous, they can exibit different: • programming languages • hardware implementations • communication protocols • The agents network is dynamic, the agents can always connect and disconnect • In order to cope with the dynamicity of the network, a special agent, the environmental majordomo, manages the connection and disconnection of agents

7. Cooperation Mechanism • COs allow agents to send messages to each other • Message = {Content, Ontology} • Through communication, two cooperation mechanisms are allowed • Reactive • Deliberative (planning)

8. Reactive Cooperation • It is based on events management • The environmental majordomo collects the registrations of environmental agents about events • When an environmental agent generates an event e, it requests to the environmental majordomo the list L(e) of the listeners for e and notifies e to the agents in L(e) • The data exchanged in notification includes the name of the event, the ontology in which the event is interpreted, and a parameter (a Java object) that contains all the other important information

9. Deliberative Cooperation When an agent wants to solve a problem, it requests the environmental majordomo to reach a goal G that expresses the problem • The environmental majordomo picks up the best decomposition for G. Each decomposition has associated three numeric indexes that express: • the efficiency, • the cost, and • the probability of success of the decomposition • The application of a decomposition to G generates a tree of goals and subgoals whose root is G and whose leaves are either primitive subgoals (operations) or non-primitive subgoals that call for further decompositions. When the leaves are all primitive, the planning tree is complete. The tree is expanded with the breadth-first search method • The environmental majordomo receives the request about G and asks all the currently connected agents to send the decompositions they know for G: {G1,G2,…,Gn} • Decompositions involve primitive or non-primitive subgoals • If a subgoal is non-primitive it must be further decomposed • When the planning tree is complete, the environmental majordomo coordinates and supervises the execution of the plan. The execution of a primitive subgoal consists in requesting the execution of the simple action associated with it to the proposing agent and waiting for the output of this execution

10. A Planning Example Goal: CheckAndRequest CreateRequestText, SearchCellNumber, SendSMS: primitive Decomposition: {IsThere, Request} IsThere: primitive If it is true then ok, else… Request: non-primitive Execution Decomposition: {CreateRequestText, SearchCellNumber, SendSMS}

11. Conclusions • A powerful and flexible architecture for dynamically integrating heterogeneous devices: the OP and CO • A robust problem solving technique that is shaped on the resources currently available • A conceptually elegant and practically effective solution to the problem of dynamically integrating networks of devices in a single multilevel system • The implementation of the above ideas in an experimental settting in which the human-computer interface is provided by a palmtop computer in line with modern tendencies in health telemonitoring

12. Future Works • Development of a distributed planner for Ambient Intelligence applications • Development of a more realistic experimental setting with real devices • Application of the environmental agency to the pacing pathology