A wide variety of Artificial Intelligence (AI) techniques, from expert systems to machine learning to robotics, are needed in the field of synthetic biology. This paper describes the design-build-test engineering cycle and lists some challenges in which AI can help.

This paper envisions a future where autonomous agents are used to foster and support pro-social behavior in a hybrid society of humans and machines. Pro-social behavior occurs when people and agents perform costly actions that benefit others. Acts such as helping others voluntarily, donating to charity, providing informations or sharing resources, are all forms of pro-social behavior. We discuss two questions that challenge a purely utilitarian view of human decision making and contextualize its role in hybrid societies: i) What are the conditions and mechanisms that lead societies of agents and humans to be more pro-social? ii) How can we engineer autonomous entities (agents and robots) that lead to more altruistic and cooperative behaviors in a hybrid society? We propose using social simulations, game theory, population dynamics, and studies with people in virtual or real environments (with robots) where both agents and humans interact. This research will constitute the basis for establishing the foundations for the new field of Pro-social Computing, aiming at understanding, predicting and promoting pro-sociality among humans, through artificial agents and multiagent systems.

Social norms can help solving cooperation dilemmas, constituting a key ingredient in systems of indirect reciprocity (IR). Under IR, agents are associated with different reputations, whose attribution depends on socially adopted norms that judge behaviors as good or bad. While the pros and cons of having a certain public image depend on how agents learn to discriminate between reputations, the mechanisms incentivizing agents to report the outcome of their interactions remain unclear, especially when reporting involves a cost (costly reputation building). Here we develop a new model---inspired in evolutionary game theory---and show that two social norms can sustain high levels of cooperation, even if reputation building is costly. For that, agents must be able to anticipate the reporting intentions of their opponents. Cooperation depends sensitively on both the cost of reporting and the accuracy level of reporting anticipation.

The objective of my research project is the formal treatment of memory issues in Intelligent Software Agents. I extend recent work which proposed a (partial) formalization of SOAR architecture in modal logic, reasoning on a particular type of agents: resource-bounded agents. I introduce explicit treatment of time instants and time intervals by means of Metric Temporal Logic, both in the background logic and in mental operations.

The Simple Temporal Problem (STP) is a fundamental temporal reasoning problem and has recently been extended to the Multiagent Simple Temporal Problem (MaSTP). In this paper we present a novel approach that is based on enforcing arc-consistency (AC) on the input (multiagent) simple temporal network. We show that the AC-based approach is sufficient for solving both the STP and MaSTP and provide efficient algorithms for them. As our AC-based approach does not impose new constraints between agents, it does not violate the privacy of the agents and is superior to the state-of-the-art approach to MaSTP. Empirical evaluations on diverse benchmark datasets also show that our AC-based algorithms for STP and MaSTP are significantly more efficient than existing approaches.

Resource constraints frequently complicate multi-agent planning problems. Existing algorithms for resource-constrained, multi-agent planning problems rely on the assumption that the constraints are deterministic. However, frequently resource constraints are themselves subject to uncertainty from external influences. Uncertainty about constraints is especially challenging when agents must execute in an environment where communication is unreliable, making on-line coordination difficult. In those cases, it is a significant challenge to find coordinated allocations at plan time depending on availability at run time. To address these limitations, we propose to extend algorithms for constrained multi-agent planning problems to handle stochastic resource constraints. We show how to factorize resource limit uncertainty and use this to develop novel algorithms to plan policies for stochastic constraints. We evaluate the algorithms on a search-and-rescue problem and on a power-constrained planning domain where the resource constraints are decided by nature. We show that plans taking into account all potential realizations of the constraint obtain significantly better utility than planning for the expectation, while causing fewer constraint violations.

In the context of fair allocation of indivisible items, fairness concepts often compare the satisfaction of an agent to the satisfaction she would have from items that are not allocated to her: in particular, envy-freeness requires that no agent prefers the share of someone else to her own share. We argue that these notions could also be defined relative to the knowledge that an agent has on how the items that she does not receive are distributed among other agents. We define a family of epistemic notions of envy-freeness, parameterized by a social graph, where an agent observes the share of her neighbours but not of her non-neighbours. We also define an intermediate notion between envy-freeness and proportionality, also parameterized by a social graph. These weaker notions of envy-freeness are useful when seeking a fair allocation, since envy-freeness is often too strong. We position these notions with respect to known ones, thus revealing new rich hierarchies of fairness concepts. Finally, we present a very general framework that covers all the existing and many new fairness concepts.

Mechanisms for aggregating the preferences of agents in elections need to balance many different considerations, including efficiency, information elicited from agents, and manipulability. We consider the utilitarian social welfare of mechanisms for preference aggregation, measured by the distortion. We show that for a particular input format called threshold approval voting, where each agent is presented with an independently chosen threshold, there is a mechanism with nearly optimal distortion when the number of voters is large. Threshold mechanisms are potentially manipulable, but place a low informational burden on voters. We then consider truthful mechanisms. For the widely-studied class of ordinal mechanisms which elicit the rankings of candidates from each agent, we show that truthfulness essentially imposes no additional loss of welfare. We give truthful mechanisms with distortion O(√m log m) for k-winner elections, and distortion O(√m log m) when candidates have arbitrary costs, in elections with m candidates. These nearly match known lower bounds for ordinal mechanisms that ignore the strategic behavior. We further tighten these lower bounds and show that for truthful mechanisms our first upper bound is tight. Lastly, when agents decide between two candidates, we give tight bounds on the distortion for truthful mechanisms.

When aggregating preferences of agents via voting, two desirable goals are to incentivize agents to participate in the voting process and then identify outcomes that are Pareto efficient. We consider participation as formalized by Brandl, Brandt, and Hofbauer (2015) based on the stochastic dominance (SD) relation. We formulate a new rule called RMEC (Rank Maximal Equal Contribution) that is polynomial-time computable, ex post efficient and satisfies the strongest notion of participation. It also satisfies many other desirable fairness properties. The rule suggests a general approach to achieving very strong participation, ex post efficiency and fairness.

Artificial agents will need to be aware of human moral and social norms, and able to use them in decision-making. In particular, artificial agents will need a principled approach to managing conflicting norms, which are common in human social interactions. Existing logic-based approaches suffer from normative explosion and are typically designed for deterministic environments; reward-based approaches lack principled ways of determining which normative alternatives exist in a given environment. We propose a hybrid approach, using Linear Temporal Logic (LTL) representations in Markov Decision Processes (MDPs), that manages norm conflicts in a systematic manner while accommodating domain stochasticity. We provide a proof-of-concept implementation in a simulated vacuum cleaning domain.