ALIAS: The Abductive LogIc AgentS architecture

ALIAS is an architecture whose goal is to introduce abduction in a (logic) multi-agent environment. It implements a distributed protocol to coordinate the reasoning of all the abductive agents in the system, inspired to the basic algorithm for abductive reasoning presented by Kakas and Mancarella. A global knowledge is represented by a set of abduced hypotheses posted in a Linda-like tuple space. A schematic representation of ALIAS architecture is here below. In ALIAS, agents are equipped with hypothetical reasoning capabilities, obtained by means of abduction. The union of their knowledge bases ends up to generate a global knowledge base, which can change in a dynamic fashion, as a consequence of agent movements. In this framework, agents can perform standard deduction and also abduce new hypotheses, provided that they are consistent with the knowledge of other agents, i.e., the global knowledge represented by the union of the agent logic programs is maintained coherent as it would be if it was owned by a single entity. To this purpose, a mechanism to coordinate agent reasoning is introduced. Agents in ALIAS are grouped into bunches; each bunch represents an agora where agents can discuss about common arguments and/or cooperate for solving particular problems. ALIAS agents can move from bunch to bunch at runtime (for this reason we call them rambling agents), and collect information (the agent experience) derived from the interactions with other agents in different agoras. More in detail, each agent is characterized by statically defined local knowledge represented by an abductive logic program and possibly by a dynamically built set of assumptions (i.e., its experience). Each bunch is represented by a set of agents and a global knowledge (i.e., the set of hypotheses assumed so far within the bunch) that is dynamically built. While static knowledge is peculiar to each agent and might differ from agent to agent, all agents in the same bunch must agree on the global set of assumed abducibles. To this purpose, a set of integrity constraints is used - together with program clauses - to confirm or discard new hypotheses. Rambling agents can contribute to enlarge the dynamic knowledge of bunches they enter. On the other hand, each rambling agent can improve its experience by moving from bunch to bunch and collecting the set of hypotheses produced in the bunches visited so far. ALIAS could be usefully employed to implement complex problem solving in distributed systems. For instance, information retrieval and filtering systems could benefit of abductive reasoning to detect inconsistencies in presence of incomplete, multiple and/or conflicting information. Moreover, bunches and Rambling Agents could also be useful in electronic commerce applications since bunches define confined, protected and possibly secure domains, where rambling agents could enter only if they have proper authorizations.