Human attitudes of objection, protest, and rebellion have undeniable potential to bring about social benefits, from social justice to healthy balance in relationships. At times, they can even be argued to be ethically obligatory. Conversely, AI rebellion is largely seen as a dangerous, destructive prospect. With the increase of interest in collaborative human/AI environments in which synthetic agents play social roles or, at least, exhibit behavior with social and ethical implications, we believe that AI rebellion could have benefits similar to those of its counterpart in humans. We introduce a framework meant to help categorize and design Rebel Agents, discuss their social and ethical implications, and assess their potential benefits and the risks they may pose. We also present AI rebellion scenarios in two considerably different contexts (military unmanned vehicles and computational social creativity) that exemplify components of the framework.

We introduce ActorSim, the Actor Simulator, a toolkit for studying situated autonomy. As background, we review three goal-reasoning projects implemented in ActorSim: one project that uses information metrics in foreign disaster relief and two projects that learn subgoal selection for sequential decision making in Minecraft. We then discuss how ActorSim can be used to address cross-disciplinary gaps in several ongoing projects. To varying degrees, the projects integrate concerns within distinct specializations of AI and between AI and other more human-focused disciplines. These areas include automated planning, learning, cognitive architectures, robotics, cognitive modeling, sociology, and psychology.

Temporal logics have been used in autonomous planningto represent and reason about temporal planning problems.However, such techniques have typically been restricted toeither (1) representing actions, events, and goals with temporalproperties or (2) planning for temporally-extended goalsunder restrictive conditions of classical planning. We introduceMixed Propositional Metric Temporal Logic (MPMTL),where formulae in MPMTL are built over mixed binary andcontinuous real variables. MPMTL provides a natural, flexibleformalism for representing and reasoning about temporalproblems. We analyze the complexity of MPMTL formulaesatisfiability and model checking, and identify MPMTLfragments with lower complexity. We also introduce an approachto world modeling using a timeline vector, relevant totemporal planning with continuous change (as opposed to theuse of discrete states). Our model supports retroactive actionprogression, concurrent and overlapping actions with discreteand continuous changes, and concurrent effects to the samevariable. For reasoning about this temporal planning problem,we define a progression function for actions with thenew temporal properties and a solution to this temporal task.