Our views on the Future of Logic Based Agents Luís Moniz Pereira José Júlio Alferes & Joint WorkshopLondon, 8 March 1999

Agents Evolution zAgents interact in increasingly complex worlds zNo longer possible to foresee and pre- program all possible situations 4Generalise from purely reactive agents to Rational Agents

Agents Evolution zAgents with complete knowledge and aptitudes are not feasible zSpecialised agents4Cooperation between agents is in order

Logic Programs for Agents zLP has developed KR mechanisms suitable for rational agents: More work on combining some or all of these mechanisms is needed 4Updates 4Condition-action rules 4Planning 4Argumentation 4Learning 4NMR mechanisms 4Taxonomies 4Abduction 4Belief revision 4Preferences

Static and Dynamic Worlds zMuch LP work has been on reasoning about a single world knowledge state zReasoning about world change and state transition is still lacking yKnowledge updates xRule inertia and rule update yAction modelling More work on transitions and updates in LP is needed

Reactive is Rational 8Agents don’t plan till deadline to act/react zReactions respond immediately to specific situations only if they have proven sure zReactions can evolve from pre-compiling abstractions of successful plans zTabling techniques are an attending tool More tabling usage and tabling of abstractions are needed

Rational monitors Reactive zRational and reactive activate together zOverriding reactions yReaction interruption and amending yResolving contradictory reactions 4Meta-level control is in order

Meta-level zLP provides an uniform language for: 4Representing reactive rules 4Knowledge representation 4Programming the meta-level zCentralised vs emergent control, or both?

Bottom-up Vs Top-down  Mix of top-down and bottom-up control è tabling comes to the rescue zMore flexibility on this admixture yConfigurable depending on situations 4Behaviour Networks [Pat Maes, c.f. summary in Artificial Minds by Stan Franklin]

Behaviour Networks zMultiple fact activation of rules zMultiple goal activation of rules zActivation propagates according to strength Both bottom-up and top-down zThe rule with the most activation fires More strength to facts è bottom-up behaviour More strength to goals è top-down behaviour

Behavioural LP zBehaviour adjustable by changing activation and other parameters zNew goals or new facts influence the system behaviour via activation spreading zBehaviour Learning: yConcepts (predicates) yRules (inductive learning) yBehaviours (rule activation, and control) with genetic algorithms?

To Do List zCombining LP mechanisms for KR zTransitions and Updates through LP zTabling usage and tabling of abstractions zMaking agent behaviour adjustable zLearning behaviour control zCooperation among specialised agents