CP-nets and Nash equilibria

We relate here two formalisms that are used for different purposes in reasoning about multi-agent systems. One of them are strategic games that are used to capture the idea that agents interact with each other while pursuing their own interest. The other are CP-nets that were introduced to express qualitative and conditional preferences of the users and which aim at facilitating the process of preference elicitation. To relate these two formalisms we introduce a natural, qualitative, extension of the notion of a strategic game. We show then that the optimal outcomes of a CP-net are exactly the Nash equilibria of an appropriately defined strategic game in the above sense. This allows us to use the techniques of game theory to search for optimal outcomes of CP-nets and vice-versa, to use techniques developed for CP-nets to search for Nash equilibria of the considered games.Comment: 6 pages. in: roc. of the Third International Conference on Computational Intelligence, Robotics and Autonomous Systems (CIRAS '05). To appea

Heuristics in Multi-Winner Approval Voting

In many real world situations, collective decisions are made using voting. Moreover, scenarios such as committee or board elections require voting rules that return multiple winners. In multi-winner approval voting (AV), an agent may vote for as many candidates as they wish. Winners are chosen by tallying up the votes and choosing the top-$k$ candidates receiving the most votes. An agent may manipulate the vote to achieve a better outcome by voting in a way that does not reflect their true preferences. In complex and uncertain situations, agents may use heuristics to strategize, instead of incurring the additional effort required to compute the manipulation which most favors them. In this paper, we examine voting behavior in multi-winner approval voting scenarios with complete information. We show that people generally manipulate their vote to obtain a better outcome, but often do not identify the optimal manipulation. Instead, voters tend to prioritize the candidates with the highest utilities. Using simulations, we demonstrate the effectiveness of these heuristics in situations where agents only have access to partial information

Simulation to Support Local Search in Trajectory Optimization Planning

NASA and the international community are investing in the development of a commercial transportation infrastructure that includes the increased use of rotorcraft, specifically helicopters and civil tilt rotors. However, there is significant concern over the impact of noise on the communities surrounding the transportation facilities. One way to address the rotorcraft noise problem is by exploiting powerful search techniques coming from artificial intelligence coupled with simulation and field tests to design low-noise flight profiles which can be tested in simulation or through field tests. This paper investigates the use of simulation based on predictive physical models to facilitate the search for low-noise trajectories using a class of automated search algorithms called local search. A novel feature of this approach is the ability to incorporate constraints directly into the problem formulation that addresses passenger safety and comfort

Heuristic Strategies in Uncertain Approval Voting Environments

In many collective decision making situations, agents vote to choose an alternative that best represents the preferences of the group. Agents may manipulate the vote to achieve a better outcome by voting in a way that does not reflect their true preferences. In real world voting scenarios, people often do not have complete information about other voter preferences and it can be computationally complex to identify a strategy that will maximize their expected utility. In such situations, it is often assumed that voters will vote truthfully rather than expending the effort to strategize. However, being truthful is just one possible heuristic that may be used. In this paper, we examine the effectiveness of heuristics in single winner and multi-winner approval voting scenarios with missing votes. In particular, we look at heuristics where a voter ignores information about other voting profiles and makes their decisions based solely on how much they like each candidate. In a behavioral experiment, we show that people vote truthfully in some situations and prioritize high utility candidates in others. We examine when these behaviors maximize expected utility and show how the structure of the voting environment affects both how well each heuristic performs and how humans employ these heuristics.Comment: arXiv admin note: text overlap with arXiv:1905.1210

Constraint-based Temporal Reasoning with Preferences

Often we need to work in scenarios where events happen over time and preferences are associated to event distances and durations. Soft temporal constraints allow one to describe in a natural way problems arising in such scenarios. In general, solving soft temporal problems require exponential time in the worst case, but there are interesting subclasses of problems which are polynomially solvable. In this paper we identify one of such subclasses giving tractability results. Moreover, we describe two solvers for this class of soft temporal problems, and we show some experimental results. The random generator used to build the problems on which tests are performed is also described. We also compare the two solvers highlighting the tradeoff between performance and robustness. Sometimes, however, temporal local preferences are difficult to set, and it may be easier instead to associate preferences to some complete solutions of the problem. To model everything in a uniform way via local preferences only, and also to take advantage of the existing constraint solvers which exploit only local preferences, we show that machine learning techniques can be useful in this respect. In particular, we present a learning module based on a gradient descent technique which induces local temporal preferences from global ones. We also show the behavior of the learning module on randomly-generated examples

Resistance to bribery when aggregating soft constraints

Abstract We consider a multi-agent scenario, where the preferences of several agents are modelled via soft constraint problems and need to be aggregated to compute a single "socially optimal" solution. We study the resistance of various ways to compute such a solution to influence the result, such as those based on the notion of bribery. In doing this, we link the cost of bribing an agent to the effort needed by the agent to make a certain solution optimal, by only changing preferences associated to parts of the solution. This leads to the definition of four notions of distance from optimality of a solution in a soft constraint problem. The notions differ on the amount of information considered when evaluating the effort

Disjunctive temporal planning with uncertainty

Driven by planning problems with both disjunctive constraints and contingency, we define the Disjunctive Temporal Problem with Uncertainty (DTPU), an extension of the DTP that includes contingent events. Generalizing existing work on Simple Temporal Problems with Uncertainty, we divide the time-points into controllable and uncontrollable classes, and propose varying notions of controllability to replace the notion of consistency.