This paper focuses on the phenomena of evolution whose appearance is notable because no explicit mutation, recombination or artificial selection operators are introduced. We call the system self-evolving because every variation is performed by the objects themselves in their machine form. Keywords: artificial chemistry, autocatalytic reaction system, molecular computing, prebiotic evolution, self-organization, self-programming 1

. A critical challenge to creating effective multi-agent systems is allowing them to operate effectively in environments where failures (`exceptions') can occur. This paper describes the motivation, progress and plans for work being pursued in this area by the MIT Adaptive Systems and Evolutionary Software research group (http://ccs.mit.edu/ases/). 1. The Challenge: Enabling Robust Open Multi-Agent Systems "open systems ... represent arguably the most important application for multiagent systems" (Wooldridge, Jennings et al. 1999) This paper addresses one simple question: how can we develop effective multi-agent systems out of the diverse and unreliable (buggy, malicious, or simply "dumb") agents and infrastructures we can expect to encounter in open system contexts? This is becoming an increasingly critical question because of emerging changes in the way human organizations work. Globalization, enabled by ubiquitous telecommunications, has increasingly required that organizations ...

contracts consisting of one or a few independent issues and tractable contract spaces. Many real-world contracts, by contrast, are much more complex, consisting of multiple inter-dependent issues and intractably large contract spaces. This paper describes a simulated annealing based approach appropriate for negotiating such complex contracts that achieves near-optimal social welfares for negotiations with binary issue dependencies.

The ‘new economy ’ of ubiquitous electronic markets entails new risks and demands novel responses. Such markets, for one, give unprecedented scope to open participation by software agents, whose unique strengths and weaknesses enable a whole range of novel failure modes. Software agents can, for example, be replicated almost indefinitely, operate extremely rapidly, and can be very difficult to trace back to their humans ‘owners’. These attributes make electronic markets susceptible to such phenomena as denial of service attacks, which have halted operations at many major businesses [1] [2], in addition to many others such as ‘bid snatching ’ (wherein a set of malicious colluding agents effectively halt another agent by using attractive bids to fraudulently ‘snatch ’ and non-perform on all the targets ’ subcontracts), ‘bid collision loops ” (where a pair of agents halt a Dutch auction by establishing an infinite loop of colliding bids), and so on. The range of attack types and corresponding responses is growing (see [3] for example) and seems limited only by human creativity. Even the mechanisms used to help avoid malicious agent behavior, such as reputation servers, are themselves prone to such attacks as collusive reputation manipulation [4]. Electronic markets also give unprecedented

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This paper develops and applies a variant of the MinConflict algorithm to the problem of sensor allocation with incomplete information for mobile robots. A categorization of the types of contention over sensing resources is provided, as well as a taxonomy of available information for the sensor scheduling task. The Min-Conflict with Happiness (MCH) heuristic algorithm, which performs sensor scheduling for situations in which no information is known about future assignments, is then described. The primary contribution of this modification to Min-Conflict is that it permits the optimization of sensor certainty over the set of all active behaviors, thereby producing the best sensing state for the robot at any given time. Data are taken from simulation experiments and runs from a pair of Nomad200 robots using the SFX hybrid deliberative /reactive architecture. Results from these experiments demonstrate that MCH is able to satisfy more sensor assignments (up to 142%) and maintain a higher overall utility of sensing than greedy or random assignments (a 7--24% increase), even in the presence of sensor failures. In addition, MCH supports behavioral sensor fusion allocations. The practical advantages of MCH include fast, dynamic repair of broken schedules allowing it to be used on computationally constrained systems, compatibility with the dominant hybrid robot architectural style, and least-disturbance of prior assignments minimizing interruptions to reactive behaviors.

Even though distributed computing and two-way communication with the customer is becoming a reality for many energy distribution companies, there still is a need to develop methodologies for more efficient energy management. In this paper we discuss current approaches to demand management, and then present ideas from other areas applied to energy management. We introduce concepts such as computational markets and software agents in this context. In addition, methods entirely based on distributed problem solving to address the computationally hard problems of resource allocation with vast number of clients are described. We also discuss how these methods can be used to perform cost/benefit analysis of demand management. 1 Demand Management By demand management we refer to the well known concept of disconnecting customer loads when there is a shortage of power in the utility system. Demand management is normally classified into two categories [4][12][18]: direct and indirect. ...

... components designed and operated without centralized control - are emerging as the dominant paradigm for creating large networked software systems in a very wide range of domains ranging from military command and control to power control systems to electronic commerce. One of the major open challenges involving in making peer-to-peer systems robust and scalable is learning how to anticipate and manage their emergent dynamics. This white paper describes a plan of work aimed at addressing this challenge.

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Even though distributed computing and two-way communication with the customer is becoming a reality for many energy distribution companies, there still is a need to develop methodologies for more efficient energy management. In this paper we discuss current approaches to demand management, and then present ideas from other areas applied to energy management. We introduce concepts such as computational markets and software agents in this context. In addition, methods entirely based on distributed problem solving to address the computationally hard problems of resource allocation with vast number of clients are described. We also discuss how these methods can be used to perform cost/benefit analysis of demand management. 1 Demand Management By demand management we refer to the well known concept of disconnecting customer loads when there is a shortage of power in the utility system. Demand management is normally classified into two categories [4][12][18]: direct and indirect. Direct demand management implies that the producer, or distributor, of energy