1. Software Development using artificial markets of constructively egoistic agents Karl Lieberherr 1SD-F09

2. Problem Solving Software examples Given a transportation task, find the optimal way of carrying it out. Given an employment market, find the optimal assignment of workers to companies. Given a Boolean formula, is it satisfiable? Given a Boolean formula in conjunctive normal form, find an assignment that maximizes the fraction of satisfied clauses. 2SD-F09

3. A new use of computers Design an artificial market AM(X) for artificial organisms (agents) that are developed by humans. X is a problem solving domain (optimization problem, decision problem). Purpose of artificial market AM(X): Develop good algorithms and useful knowledge about a problem solving domain X. The market leader has the best algorithm and the best knowledge compared to all the other agents. 3SD-F09

4. Traditional Approach Human Developers Develop new software for problem solving domain X Static Evaluation. No competition. human1human2 Testing unit testing integration testing Benchmark is used to evaluate software human3human4 Users Requirements for X 4SD-F09

5. Why Software Development through an artificial market? Human Developers Develop new software for problem solving domain X Artificial Market(X) Erika-Patrick-agent winning-agent Evaluates fairly, frequently, constructively and dynamically. Drives innovation. Challenges humans. Agents point humans to what needs attention in the software. human1human2 ErikaPatrick Benchmark is used to evaluate software Users Requirements for X 5SD-F09

6. Erika-Patrick Agent Surrogate of combined knowledge of Erika and Patrick successfully transferred to agent. Transfer knowledge by programming. 6SD-F09

7. Agent design How to design an artificial organism? – Has a basic need: maximize life energy, money. – Has a rhythm. Every round the same activity happens. – Interacts with other agents by offering and buying challenges. Offering and buying a challenge might create a big win but it also makes the agent vulnerable to a loss. There is uncertainty when offering or buying a challenge. 7SD-F09

8. Agent design challenge = (belief, price) Belief expresses a fact about problem solving domain X believed to be true. Belief is result of experiments with problem solving algorithm. If problem solving algorithm is not good (has bugs, is inefficient compared to others), the belief can be discounted by buyer/acceptor of challenge. If belief is successfully discounted, the seller has to pay more than the initial price to buyer. If belief is not discounted, the buyer lost the price he paid. 8SD-F09

9. Which means Agents should only buy a challenge whose belief they think they can discount. Agents should only offer a challenge if they can successfully support the belief against a buyer. 9SD-F09

10. Beliefs Belief is supported: By giving problem in domain X to buyer for which it cannot find a solution that contradicts the belief. Belief is discounted: By giving problem in domain X to buyer for which it can find a solution that contradicts the belief. Belief language needs to be carefully chosen. 10SD-F09

11. Agents are constructively egoistic They have an ego. They can constructively show that they are better than other agents by playing the game. They give constructive feedback to other agents. The social welfare consists of improving software/algorithms. 11SD-F09

12. SCG(X) X: optimization problem. Classic beliefs: – I believe that I can give you a problem satisfying predicate pred so that you cannot find an assignment satisfying the fraction q of all constraints. (exists p in pred for all J fsat(p,J) < q) – I believe that for all problems satisfying predicate pred I can find an assignment satisfying the fraction q of all constraints. (for all p in pred exists J fsat(p,J) >= q) – Is one the negation of the other? 12SD-F09