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University College Dublin SCHOOL OF COMPUTER SCIENCE & INFORMATICS Multi-Agent Systems(MAS) & Distributed Artificial Intelligence(DAI) G.M.P. O’Hare Lectures 5 & 6 © G.M.P O'Hare School of Computer Science & Informatics Distributed Artificial Intelligence Distributed Artificial Intelligence(DAI) :-Endeavours to achieve Intelligent Systems not by constructing a large KnowledgeBased System, but rather by partitioning the knowledge domain and developing 'Intelligent Agents',each exhibiting expertise in a particular domain fragment. This group of agents will thereafter collectively work towards the solution of global problems. © G.M.P O'Hare School of Computer Science & Informatics The Co-operating Experts Metaphor This solution of problems by a group of agents, providing mutual assistance as and when necessary is often referred to as the..... "Community of Co-operating Experts Metaphor" Smith and Davis, Lenat, Hewitt Proponents of this philosophy believe that reciprocal cooperation is the cornerstone of society. © G.M.P O'Hare School of Computer Science & Informatics Social Agents Domain Specific Knowledge Base Q? M? L 5 S 4 R AND P -> Q M 2.4 3 P? R 4 P 6 6 2 L OR S -> M M -> P S? L? R? Aquuaintance Model 4 5 © G.M.P O'Hare School of Computer Science & Informatics Why Distributed Artificial Intelligence? Mirrors Human Cognition Potential Performance Enhancements Elegantly Reflects Society Incremental Development Increased Robustness Reflects Trends in Computer Science in General Strong Analogies to Decompositional Techniques employed in Software Engineering © G.M.P O'Hare School of Computer Science & Informatics Coordination Paradigms Numerous Different Paradigms have been proposed.... The Blackboard Model (Reddy et al) The Actor Model (Hewitt) The Contract Net Approach (Smith and Davis) The 'BEING' Concept (Lenat) Hybrid Approaches © G.M.P O'Hare School of Computer Science & Informatics The Blackboard Model DAI first presented itself in the form of a blackboard model in the HEARSAY I,II and III (Carnegie Mellon Univ) speech understanding systems The Blackboard model involves agents communicating by way of a shared global data structure called the 'Blackboard' Agents could not communicate directly with each other but only via the contents of the blackboard. © G.M.P O'Hare School of Computer Science & Informatics Poll-Hypothesis-Test The Model generally involves a poll-hypothesis-test cycle. In the case of the HEARSAY systems this consisted of.... Initially a hypothesis is installed on the Blackboard regarding the meaning of a particular utterance poll :- each ks is polled in order to ascertain if it can refine the. hypothesis Those which can indicate their ability to do so with an associated confidence factor hypothesis :- The ks boasting the highest cf is invoked making the appropriate refinement to the hypothesis on the blackboard. test :- The other kss evaluate the amendment and based on the collected response the amendment is either adopted or ignored The hypothesis is successively refined repeating this cycle until the utterance is identified with a sufficient degree of confidence © G.M.P O'Hare School of Computer Science & Informatics Blackboard Problems DAI approaches seem to have followed a course similar to those developments which have taken place in the design of real-time languages The blackboard exhibits obvious similarities to Hoare's Monitor concept and in general the Mail-box concept It also suffers from the same limitations :• Congestion Problems • Reliability Problems Thus there was a realisation as in real-time language design that.... "To divorce data transmission and process synchronisation was totally unnatural" Young, Real-Time Languages © G.M.P O'Hare School of Computer Science & Informatics The Actor Model Hewitt's Actor Model was one such example of later synchronised approaches The society of co-operating experts were to be modelled by a basic building block called an Actor Actors could communicate with other Actors via messages The behaviour of an actor was to be defined dependent upon which message it received and these potential actions are contained in a Script Actors could communicate with those actors with whom they were acquainted as indicated by way of their acquaintance list This model therefore offered point to point communication © G.M.P O'Hare School of Computer Science & Informatics The Contract Net Protocol I The Contract Net Approach regarded the distribution of tasks amongst agents, or the connection problem as a process of negotiation. Contract Net Protocol:- A node(the manager) advertises a problem via a broadcast to all other nodes(potential contractors). Potential contractors compare this and other problems and upon identification of the tasks for which they are most suited they submit bids. The manager evaluates bids and awards contract accordingly © G.M.P O'Hare School of Computer Science & Informatics Contract Net II The communication protocol is vastly superior in that both manager and potential contractor participate in the decision regarding a suitable contract It is also worth noting that here three addressing modes are offered: general broadcast, limited broadcast and point to point. This approach adopts an inter agent language which while simple is capable of supporting the relevant communication. © G.M.P O'Hare School of Computer Science & Informatics Beings Lenat commissioned a very different approach when trying to overcome problems of inter agent understanding. In his PUP6 system an agent was represented by a 'Being' which had to comply with a predefined structure. It consisted of a fixed number of 'parts', each part representing a question that the ks may be equipped to answer. If a part contained a value then the being is sufficiently knowledgeable with the value representing a procedural attachment which would yield the appropriate answer. When a being asks a question it must therefore stipulate the relevant part. This approach made question answering relatively trivial - essentially pattern matching. It avoided the need for an inter node language, however in so doing it enforced a very stylised form on each agent Furthermore there was no provision for gaining expertise. © G.M.P O'Hare School of Computer Science & Informatics Hybrid Approaches Hybrid Approaches - Lesser and Corkill 1981 - Huhns et al 1983 © G.M.P O'Hare School of Computer Science & Informatics Benevolence & Competition Within all of these approaches there is this underlying presumption that the intelligent agents necessarily want to co-operate. "The Benevolent Agent Assumption" More recently a school of thought believes that this is not necessarily the case and agents may have conflicting goals This has resulted in .... "The Conflicting Agents Assumption" Geneserth and Rosenchien Stanford HPP Project © G.M.P O'Hare School of Computer Science & Informatics Problems with DAI • Identification of appropriate task decomposition and task distribution strategies • Optimisation of problem solution (Cammarata et al 1982,1983) • Difference of opinion between experts where the mapping between expertise and experts is not 1: 1 but 1: n - need conflict resolution strategies • Problems with understanding • Handling uncertainty becomes even more problematic • Need deadlock avoidance strategies • Problems with heterogenous nodes • Interoperability © G.M.P O'Hare School of Computer Science & Informatics Reactive v Classical Systems Essentially Multi-Agent systems occupy a point on a continuum between two extreme classes of system. These two extremes are... • The classical system • The reactive or situated action system We propose a compromise that of the 'Deliberate Social Agent' © G.M.P O'Hare School of Computer Science & Informatics Classical System Contemplative Reasoning X Deliberate Social System X Complexity Reactive Reactive Situated Action System X Internal Model Percieve the world © G.M.P O'Hare Represent the world School of Computer Science & Informatics Reactive or Situated Systems Agents react to varying situations and consequently do not have an explicit representation of the world within which they exist. Reasoning takes place within each agent at a very low level, essentially each agent has little more than an ability to perform pattern matching. A given situation is characterised and matched against a collection of rules specifying appropriate behaviour associated with each of these situations ie situation -action or situated action. Typically the actions associated with a given situation are often very simple and consequently the agents themselves are very simple computational entities. Even though each of the individual agents are very simple the global complexity and global structures can be achieved as a result of the emergent property of the interacting behaviours of the community of agents. © G.M.P O'Hare School of Computer Science & Informatics Reactive Systems Assessment Advantages * simplicity. * avoidance of necessity for a sophiciated representation of the world and more significantly the problems of maintaining this model. * generally the structure of agent interaction is well defined and domain independent. Disadvantages * New sets of rules need to be designed for each application. * Each situation needs to be specified and identified so as to have an associated rule. * Difficulty in solving inherently recursive problems. * Lack of a precise theory upon which the combining behaviours of agents can be based and explained. © G.M.P O'Hare School of Computer Science & Informatics Reflective Systems Generally the agents within a reflective system are more complex computational entities. They do not merely react to a given situation in a specific way. In fact they may often react in different ways dependent on their own ‘beliefs’ or ‘intentions’. Such systems necessitate an internal representation of the world. They often base their reasoning on the actions of the other agents within the community. They normally possess some model of intentionality which represents their goals, desires, prejudices, beliefs etc. about themselves and the remainder of the community. Certain classes of problem seem to necessitate this ability to reason using intentionality. The ‘wisest man’ puzzle seems to typify these. © G.M.P O'Hare School of Computer Science & Informatics Reflective Systems II Reasoning intentionally normally demands use of higher order logics. Particularly Modal logics. - epistemic logics - doxastic logics There are two general approaches Sentential logics (Konolidge) Possible World Logics (Kripke) © G.M.P O'Hare School of Computer Science & Informatics Deliberative Systems Assessment Advantages A clear theoretical reasoning model that underpins the approach; A mental state that is verifiable and traceable; Amenable to modeling using higher order logics; Disadvantages Theoretical Model is complex and unwieldy; Approach is more computationally demanding; Less appropriate for time critical reasoning scenarios; Necessitates the maintenance of a model of the environment; © G.M.P O'Hare School of Computer Science & Informatics The Cognitive Chasm Exotic, Formal Complex Computationally Intractable Logics of Intention Pragmatic, Shallow, Simpistlic Multi-agent Implementation testbeds with little conformance to complex theoretical models The 'cognitive chasm' that this thesis is seeking to bridge © G.M.P O'Hare School of Computer Science & Informatics