Here - Agents Lab - University of Nottingham

or relative performance of each platform, but to estimate the relative average number ofqueries and updates performed by ‘typical’ agent programs, and their relative averagecosts on each platform, in order to determine to what extent caching may be useful as ageneral strategy for logic-based agent programming languages. To this end, we selectedthree well known agent platforms (Jason [6], 2APL [7] and GOAL [8]), and five existingagent programs/environments (Blocks World, Elevator Sim, Multi-Agent ProgrammingContest 2006 & 2011, and Wumpus World).The agent platforms were chosen as representative of the current state of the artin logic-based agent programming languages, and span a range of implementation approaches.For example, both 2APL and GOAL use Prolog engines provided by thirdparties for knowledge representation and reasoning. 2APL uses the commercial Prologengine JIProlog [10] implemented in Java, whereas GOAL uses the Java interface JPLto connect to the open source SWI-Prolog engine (v5.8) which is implemented in C[11]. In contrast, the logical language used in Jason is integrated into the platform andis implemented in Java.The agent programs were chosen as representative of ‘typical’ agent applications,and span a wide range of task environments (from observable and static to partiallyobservable and real-time), program complexity (measured in lines of code, LoC), andprogramming styles. The Blocks World is a classic environment in which blocks mustbe moved from an initial position to a goal state by means of a gripper. The BlocksWorld is a single agent, discrete, fully observable environment where the agent has fullcontrol. Elevator Sim is a dynamic, environment that simulates one or more elevators ina building with a variable number of floors (we used 25 floors) where the goal is to transporta pre-set number of people between floors [12]. Each elevator is controlled by anagent, and the simulator controls people that randomly appear, push call buttons, floorbuttons, and enter and leave elevators upon arrival at floors. The environment is partiallyobservable as elevators cannot see which buttons inside other elevators are pressed norwhere these other elevators are located. In the 2006 Multi-Agent Programming Contestscenario (MAPC 2006) [13] teams of 5 agents explore grid-like terrain to find gold andtransport it to a depot. In the 2011 Multi-Agent Programming Contest scenario (MAPC2011) [14] teams of 10 agents explore ‘Mars’ and occupy valuable zones. Both MAPCenvironments are discrete, partially observable, real-time multi-agent environments, inwhich agent actions are not guaranteed to have their intended effect.Finally, the Wumpus World is a discrete, partially observable environment in whicha single agent must explore a grid to locate gold while avoiding being eaten by theWumpus or trapped in a pit. For some of the environments we also varied the size ofthe problem instance the agent(s) have to deal with. In the Blocks World the numberof blocks determines the problem size, and in the Elevator Sim an important parameterthat determines the size of a problem instance is the number of people to be movedbetween floors. The size of problem instances that we have used can be found in thefirst column of Tables 2 through 6.It is important to stress that, to avoid any bias due to agent design in our results,the programs were not written specially for the experiments. While our selection wastherefore necessarily constrained by the availability of pre-existing code (in particularversions of each program were not available for all platforms), we believe our results are122